31:Tbad,plotHeatmapParticipation = { const data = task_gender_data; const margin = { top: 30, right: 0, bottom: 5, left: 90 }; const width = 475 const height = 250 const tooltip = d3 .select("body") .append("div") .attr("class", "plotTooltip") .style("position", "absolute"); const svg = d3 .create('svg') .attr("viewBox", [ 0, 0, width + margin.left + margin.right, height + margin.top + margin.bottom ]) // .attr("width", "%") .attr("height", "100%"); const xScale = d3 .scaleBand() .domain(data.map((d) => d.country)) .range([margin.left, width - margin.right]) .padding(0.05); const yScale = d3 .scaleBand() .domain(["Land Prep.", "Planting/Weeding", "Harvesting"]) .range([margin.top, height - margin.bottom]) .padding(0.05); // const colorScale = d3.scaleSequential(d3.interpolateBlues).domain([0, 100]); const colorScale = d3 .scaleLinear() .domain([21, 61]) .range(["#F7D732", "#216729"]); svg .selectAll("rect") .data( data.flatMap((d) => ["Land Prep.", "Planting/Weeding", "Harvesting"].map((type) => ({ country: d.country, type, value: d[type] })) ) ) .join("rect") .attr("x", (d) => xScale(d.country)) .attr("y", (d) => yScale(d.type)) .attr("width", xScale.bandwidth()) .attr("height", yScale.bandwidth()) .attr("fill", (d) => (d.value !== null ? colorScale(d.value) : "white")) // Set fill to white for null values .on("mousemove", (event, d) => { tooltip .style("top", event.pageY - 10 + "px") .style("left", event.pageX + 10 + "px") .style("opacity", 1) .html( `${_lang(nbText.General.adminNames[d.country])}
${_lang(nbText.GenderedAfriAG.FemaleParticipationLabor.plot.practices[d.type])}: ${ d.value !== null ? d.value + "%" : _lang(nbText.General.noDataAvailable) }` ); }) .on("mouseout", function (event, d) { tooltip.style("opacity", 0); }); svg .append("g") .attr("transform", `translate(0, ${height - margin.bottom})`) .attr("stroke-width", ".5px") .call(d3.axisBottom(xScale).tickFormat((d) => _lang(nbText.General.adminNames[d]))) .selectAll("text") .style("font-size", "8px"); const yAxis = svg .append("g") .attr("transform", `translate(${margin.left}, 0)`) .attr("stroke-width", ".5px") .call(d3.axisLeft(yScale).tickFormat((d) => (_lang(nbText.GenderedAfriAG.FemaleParticipationLabor.plot.practices[d])))) .selectAll("text") .style("font-size", "8px"); // const legend = svg.node().appendChild(heatmap_legend.cloneNode(true)); // // const legend = svg.append(heatmap_legend); // legend.attr("transform", `translate(${100}, ${-15}) scale(.75)`); svg .append("g") .attr("transform", `translate(85,-10) scale(0.70)`) .append(heatmap_legend); return svg.node(); }32:T70d,genderCropHeadline = { const container = html`

`; let text; // let fill_items = [{ name: "field", value: "hello world!" }]; let fill_items if (hover_data === null) { text = md`Across these 6 countries, Women are most involved with the production of **Roots & Tubers** at **42%**. This is worth an estimated **Int${formatDollar(root_tube_total)}**.`; text = md`${_lang(nbText.GenderedAfriAG.WomenDominate.headline.default)}` } else if (hover_data.value !== null) { const involvementLevel = hover_data.value > crop_gender_means["country_mean"][hover_data.country] ? "more" : "less"; const productionValue = formatDollar(selection_total * (hover_data.value / 100)); fill_items = [{ name: "country", value: _lang(nbText.General.article_adminNames[hover_data.country]) }, { name: "involvement", value: _lang(nbText.General[involvementLevel]) }, { name: "cropType", value: _lang(nbText.GenderedAfriAG.FemaleParticipationCrops.plot.crops[hover_data.type]) }, { name: "cropPCT", value: hover_data.value }, { name: "cropValue", value: productionValue }]; let template = _lang(nbText.GenderedAfriAG.WomenDominate.headline.valid_hover) text = md`${Lang.reduceReplaceTemplateItems(template, fill_items)}` } else { let template = _lang(nbText.GenderedAfriAG.WomenDominate.headline.null_hover) fill_items = [{ name: "country", value: _lang(nbText.General.article_adminNames[hover_data.country]) }, { name: "cropType", value: _lang(nbText.GenderedAfriAG.FemaleParticipationCrops.plot.crops[hover_data.type])}] text = md`${Lang.reduceReplaceTemplateItems(template, fill_items)}` } container.append(text); return container; };33:Tcd3,plotHeatmapProduction = { const data = crop_gender_data; const margin = { top: 40, right: 0, bottom: 5, left: 100 }; const width = 475 const height = 320 const tooltip = d3 .select("body") .append("div") .attr("class", "plotTooltip") .style("position", "absolute"); const svg = d3 .create("svg") .attr("viewBox", [ 0, 0, width + margin.left + margin.right, height + margin.top + margin.bottom ]) // .attr("width", "85%") .attr("height", "90%"); const xScale = d3 .scaleBand() .domain(data.map((d) => d.country)) .range([margin.left, width - margin.right]) .padding(0.05); const yScale = d3 .scaleBand() .domain([ "Cereals", "Legumes", "Roots & Tubers", "Fruits & Vegetables", "Non-edible crops" ]) .range([margin.top, height - margin.bottom]) .padding(0.05); // const colorScale = d3.scaleSequential(d3.interpolateBlues).domain([0, 100]); const colorScale = d3 .scaleLinear() .domain([21, 61]) .range(["#F7D732", "#216729"]); svg .selectAll("rect") .data( data.flatMap((d) => [ "Cereals", "Legumes", "Roots & Tubers", "Fruits & Vegetables", "Non-edible crops" ].map((type) => ({ country: d.country, type, value: d[type] })) ) ) .join("rect") .attr("x", (d) => xScale(d.country)) .attr("y", (d) => yScale(d.type)) .attr("width", xScale.bandwidth()) .attr("height", yScale.bandwidth()) .attr("fill", (d) => (d.value !== null ? colorScale(d.value) : "white")) .on("mouseover", function (event, d) { mutable hover_data = d; // tooltip // .style("top", event.pageY - 2 + "px") // .style("left", event.pageX + 2 + "px") // .style("opacity", 1) // .text(`${d.country} - ${d.type}: ${ // d.value !== null ? d.value + "%" : "No data available" // }`); }) .on("mousemove", (event, d) => { tooltip .style("top", event.pageY - 10 + "px") .style("left", event.pageX + 10 + "px") .style("opacity", 1) .html( `${_lang(nbText.General.adminNames[d.country])}
${_lang(nbText.GenderedAfriAG.FemaleParticipationCrops.plot.crops[d.type])}: ${ d.value !== null ? d.value + "%" : _lang(nbText.General.noDataAvailable) }` ); }) .on("mouseout", function (event, d) { mutable hover_data = null; tooltip.style("opacity", 0); }); svg .append("g") .attr("transform", `translate(0, ${height - margin.bottom})`) .attr("stroke-width", ".5px") .call(d3.axisBottom(xScale).tickFormat((d) => _lang(nbText.General.adminNames[d]))) .selectAll("text") .style("font-size", "8px"); svg .append("g") .attr("transform", `translate(${margin.left}, 0)`) .attr("stroke-width", ".5px") .call(d3.axisLeft(yScale).tickFormat((d) => _lang(nbText.GenderedAfriAG.FemaleParticipationCrops.plot.crops[d]))) .selectAll("text") .style("font-size", "8px"); svg .append("g") .attr("transform", `translate(100,-10) scale(0.75)`) .append(heatmap_legend); return svg.node(); }34:T167c,{ const margin = { top: 10, right: 70, bottom: 35, left: 85 }; const width = 350 - margin.left - margin.right; const height = 200 - margin.top - margin.bottom; const lollipop_admin = admin1_sel === "Full Country" ? _lang(nbText.General.adminNames[country]) : admin1_sel; const tooltip = d3 .select("body") .append("div") .attr("class", "plotTooltip") .style("position", "absolute"); let tooltipText; if (admin1_sel !== "Full Country") { tooltipText = (d) => `${_lang(nbText.GenderInvestments.GenderIndex.plot.domains[d])}
${lollipop_admin} ${_lang(nbText.General.mean)}: ${filteredData[d].toFixed( 2 )}
${region_name} ${_lang(nbText.General.mean)}: ${regional_avg[d].toFixed(2)}`; } else { tooltipText = (d) => `${_lang(nbText.GenderInvestments.GenderIndex.plot.domains[d])}
${lollipop_admin} ${_lang(nbText.General.mean)}: ${filteredData[d].toFixed( 2 )}`; } const svg = d3 .create("svg") .attr("viewBox", [ 0, 0, width + margin.left + margin.right, height + margin.top + margin.bottom ]) .attr("width", "90%") .attr("height", "90%"); const g = svg .append("g") .attr("transform", `translate(${margin.left},${margin.top})`); const data = roi[0]; const variablesToKeep = [ "empowerment_index", "decisions", "domestic_violence", "education", "employment", "family_planning", "reproductive_health" ]; const filteredData = Object.fromEntries( Object.entries(data).filter(([key, value]) => variablesToKeep.includes(key)) ); const variables = Object.keys(filteredData); variables.sort( (a, b) => variablesToKeep.indexOf(a) - variablesToKeep.indexOf(b) ); const region_name = country === "SSA" || admin1_sel === "Full Country" ? "SSA" : country; const xScale = d3.scaleLinear().domain([0, 1]).range([0, width]); const yScale = d3 .scaleBand() .domain(variables) .range([0, height]) .padding(0.1); let countryLine_offset = 2; if (admin1_sel !== "Full Country") { countryLine_offset = 2.75; const meanLine_offset = 1.5; const meanLine = g .selectAll(".mean-line") .data(variables) .enter() .append("line") .attr("class", "mean-line") .attr("x1", 0) .attr("x2", (d) => xScale(regional_avg[d])) .attr( "y1", (d) => yScale(d) + yScale.bandwidth() / meanLine_offset ) .attr( "y2", (d) => yScale(d) + yScale.bandwidth() / meanLine_offset ) .attr("stroke", "grey") .attr("stroke-width", 0.75); meanLine .on("mousemove", (event, d) => { tooltip .style("top", event.pageY - 10 + "px") .style("left", event.pageX + 10 + "px") .style("opacity", 1) .html(tooltipText(d)); }) .on("mouseout", function (event, d) { tooltip.style("opacity", 0); }); const meanDot = g .selectAll(".mean-dot") .data(variables) .enter() .append("circle") .attr("class", "mean-dot") .attr("cx", (d) => xScale(regional_avg[d])) .attr( "cy", (d) => yScale(d) + yScale.bandwidth() / meanLine_offset ) .attr("r", 2) .attr("fill", "grey"); meanDot .on("mousemove", (event, d) => { tooltip .style("top", event.pageY - 10 + "px") .style("left", event.pageX + 10 + "px") .style("opacity", 1) .html(tooltipText(d)); }) .on("mouseout", function (event, d) { tooltip.style("opacity", 0); }); g.append("g") .attr("transform", `translate(195, 70) scale(0.35)`) .append(loli_legend); } const line = g .selectAll(".line") .data(variables) .enter() .append("line") .attr("class", "line") .attr("x1", 0.1) .attr("x2", (d) => xScale(filteredData[d])) .attr( "y1", (d) => yScale(d) + yScale.bandwidth() / countryLine_offset ) .attr( "y2", (d) => yScale(d) + yScale.bandwidth() / countryLine_offset ) .attr("stroke", "#57832b") .attr("stroke-width", 0.75); line .on("mousemove", (event, d) => { tooltip .style("top", event.pageY - 10 + "px") .style("left", event.pageX + 10 + "px") .style("opacity", 1) .html(tooltipText(d)); }) .on("mouseout", function (event, d) { tooltip.style("opacity", 0); }); const dot = g .selectAll(".dot") .data(variables) .enter() .append("circle") .attr("class", "dot") .attr("cx", (d) => xScale(filteredData[d])) .attr( "cy", (d) => yScale(d) + yScale.bandwidth() / countryLine_offset ) .attr("r", 3) .attr("fill", "#57832b"); dot .on("mousemove", (event, d) => { tooltip .style("top", event.pageY - 10 + "px") .style("left", event.pageX + 10 + "px") .style("opacity", 1) .html(tooltipText(d)); }) .on("mouseout", function (event, d) { tooltip.style("opacity", 0); }); g.append("g") .attr("transform", `translate(0,${height})`) .call(d3.axisBottom(xScale)) .attr("stroke-width", ".5px") .attr("font-size", "8px"); g.append("text") .attr("transform", `translate(${width / 2}, ${height + margin.bottom - 5})`) .style("text-anchor", "middle") .text(_lang(nbText.GenderInvestments.GenderIndex.plot.title_X)) .attr("font-size", "8px"); g.append("g") .call(d3.axisLeft(yScale).tickSize(0).tickFormat((d) => _lang(nbText.GenderInvestments.GenderIndex.plot.domains[d]))) .attr("stroke-width", ".5px") .attr("font-size", "8px"); return svg.node(); }35:T475,viewof BiVariable_form = Inputs.form( { hazard: Inputs.radio(["human", "crop", "livestock"], { value: "human", label: html`${_lang(nbText.GenderHotspots.selectors.heatHazard.title)}`, format: (x) => html`

${hs_icons[x].cloneNode(true)} ${_lang(nbText.GenderHotspots.selectors.heatHazard.hazards[x])}

` }), Gender_var: Inputs.bind( Inputs.select( // new Map(Object.entries(F_dict).map(([key, value]) => [value, key])), Object.keys(F_dict), { label: _lang(nbText.GenderHotspots.selectors.GenderVar.title), value: "women_in_agric", format: (x) => _lang((nbText.GenderHotspots.selectors.GenderVar.domains[x])) } ), viewof sel_gender_var ) }, { template: ( inputs ) => htl.html`

${Object.values(inputs)}

` } )36:T1ab3,viewof Bivariate_map = { const height = 610; const width = 775; const padding = 20; let x; if (country === "SSA") { x = d3.scaleQuantile( Array.from( BiVar_merge.filter((d) => d.admin1_name === null), (d) => d[G_variable] ), d3.range(3) ); } else { x = d3.scaleQuantile( Array.from( BiVar_merge.filter((d) => d.admin1_name !== null), (d) => d[G_variable] ), d3.range(3) ); } const haz_threshold = [ hazard_bivariate_thresholds[heat_hazard]["low"], hazard_bivariate_thresholds[heat_hazard]["high"] ]; d3.select(".bivariateTip").remove(); const tooltip = d3 .select("body") .append("div") .attr("class", "plotTooltip bivariateTip") .style("position", "absolute"); const y = d3.scaleThreshold().domain(haz_threshold).range(d3.range(3)); const projection = d3.geoMercator(); const geo_path = d3.geoPath().projection(projection); const color = d3.scaleOrdinal().domain(d3.range(9)).range(bi_color); const a0_features = topojson.feature( admin0_boundaries, admin0_boundaries.objects["atlas-region_admin0_harmonized"] ); projection.fitExtent( [ [padding, padding], [width - padding, height - padding] ], a0_features ); const svg = d3 .create("svg") .attr("viewBox", [0, 0, 900, 680]) .attr("title", "Map") .on("dblclick", resetView); const a0_paths = svg .append("g") .selectAll("path") .data(a0_features.features) .join("path") .attr("stroke", "white") .attr("stroke-width", "1px") .attr("class", "admin0-boundary") .attr("fill", (d) => { const a0_data = index.get(d.properties.admin0_name).get(null); return color(x(a0_data[G_variable]) * 3 + y(a0_data.hs_value)); }) .attr("d", geo_path) .on("click", clicked); a0_paths .on("mousemove", (event, d) => { tooltip .style("top", event.pageY - 10 + "px") .style("left", event.pageX + 10 + "px") .style("opacity", 1) .html(() => { const admin0_name = d.properties.admin0_name; const dataEntry = index.get(admin0_name).get(null); const heatStressText = `${_lang(nbText.GenderHotspots.plot.heatLabels[heat_hazard])}: ${dataEntry.hs_value.toFixed( 2 )} (${_lang(nbText.General[labels[y(dataEntry.hs_value)]])})`; let variableText; if (G_variable === "women_in_agric") { variableText = `${_lang(nbText.GenderHotspots.selectors.GenderVar.domains[G_variable])}: ${dataEntry[ G_variable ].toLocaleString(undefined, { maximumFractionDigits: 0 })} (${ _lang(nbText.General[labels[x(dataEntry[G_variable])]]) })`; } else { variableText = `${_lang(nbText.GenderHotspots.selectors.GenderVar.domains[G_variable])}: ${dataEntry[ G_variable ].toFixed(2)} (${_lang(nbText.General[labels[x(dataEntry[G_variable])]])})`; } return `${_lang(nbText.General.adminNames[admin0_name])}
${heatStressText}
${variableText}`; }); }) .on("mouseout", function (event, d) { mutable hover_data = null; tooltip.style("opacity", 0); }); const mesh = svg .append("path") .datum( topojson.mesh( admin0_boundaries, admin0_boundaries.objects["atlas-region_admin0_harmonized"], (a, b) => a !== b ) ) .attr("fill", "none") .attr("stroke", "black") .attr("stroke-linejoin", "round") .attr("d", geo_path); if (clickedAdmin0 !== null) { const padding_a1 = 150; const admin1_features = topojson .feature( admin1_boundaries, admin1_boundaries.objects["atlas-region_admin1_harmonizedV2"] ) .features.filter( (feature) => feature.properties.admin0_name === clickedAdmin0 ); const admin1_geojson = { type: "FeatureCollection", features: admin1_features }; const a1_paths = svg .append("g") .selectAll("path") .data(admin1_features) .join("path") .attr("stroke", "white") .attr("stroke-width", "1px") .attr("fill", (d) => { const admin0_map = index.get(d.properties.admin0_name); const a1_data = admin0_map.get(d.properties.admin1_name); return color(x(a1_data[G_variable]) * 3 + y(a1_data.hs_value)); }) .attr("class", "admin1-boundary"); a1_paths .on("mousemove", (event, d) => { tooltip .style("top", event.pageY - 10 + "px") .style("left", event.pageX + 10 + "px") .style("opacity", 1) .html(() => { const dataEntry = index .get(d.properties.admin0_name) .get(d.properties.admin1_name); const heatStressText = `${_lang(nbText.GenderHotspots.plot.heatLabels[heat_hazard])}: ${dataEntry.hs_value.toFixed( 2 )} (${_lang(nbText.General[labels[y(dataEntry.hs_value)]])})`; let variableText; if (G_variable === "women_in_agric") { variableText = `${_lang(nbText.GenderHotspots.selectors.GenderVar.domains[G_variable])}: ${dataEntry[ G_variable ].toLocaleString(undefined, { maximumFractionDigits: 0 })} (${ _lang(nbText.General[labels[x(dataEntry[G_variable])]]) })`; } else { variableText = `${_lang(nbText.GenderHotspots.selectors.GenderVar.domains[G_variable])}: ${dataEntry[ G_variable ].toFixed(2)} (${_lang(nbText.General[labels[x(dataEntry[G_variable])]])})`; } return `${d.properties.admin1_name}, ${_lang(nbText.General.adminNames[d.properties.admin0_name])}
${heatStressText}
${variableText}`; }); }) .on("mouseout", function (event, d) { mutable hover_data = null; tooltip.style("opacity", 0); }); projection.fitExtent( [ [padding_a1, padding_a1], [width - padding_a1, height - padding_a1] // This is where the translate happens + 250 ], admin1_geojson ); a0_paths.attr("visibility", "hidden"); svg.selectAll("path").attr("d", geo_path); } const legend = svg .append("g") .attr("transform", `translate(-30,${height - 210}) scale(1)`) .append(bi_legend); return svg.node(); function resetView() { mutable clickedAdmin0 = null; projection.fitExtent( [ [padding, padding], [width - padding, height - padding] ], a0_features ); svg.selectAll("path").attr("d", geo_path); } function clicked(event, d) { mutable clickedAdmin0 = d.properties.admin0_name; } }37:Td96,biVar_insight = { let filtered = country === "SSA" ? BiVar_merge.filter((d) => d.admin1_name === null).map((d) => ({ ...d, admin_name: d.admin0_name })) : BiVar_merge.filter( (d) => d.admin0_name === country && d.admin1_name !== null ).map((d) => ({ ...d, admin_name: d.admin1_name })); const heat_HotSpot = filtered.reduce( (max, obj) => obj.hs_value > max.hs_value ? { admin_name: obj.admin_name, hs_value: obj.hs_value, ac_value: obj[G_variable] } : max, { admin_name: null, hs_value: -Infinity } ); const classifyHSValue = (value) => { const thresholds = hazard_bivariate_thresholds[heat_hazard]; return value < thresholds.low ? "low" : value <= thresholds.high ? "moderate" : "high"; }; const classifyACValue = (value) => { const values = country === "SSA" ? BiVar_merge.filter((d) => d.admin1_name === null).map((d) => d[G_variable]) : BiVar_merge.filter((d) => d.admin1_name !== null).map((d) => d[G_variable]); const val_class = d3.scaleQuantile(values, d3.range(3))(value); return val_class === 0 ? "low" : val_class === 1 ? "moderate" : "high"; }; const gender_HotSpot = filtered.reduce( (extreme, obj) => G_variable === "women_in_agric" ? obj[G_variable] > extreme.value ? { admin_name: obj.admin_name, value: obj[G_variable], hs_value: obj.hs_value } : extreme : obj[G_variable] < extreme.value ? { admin_name: obj.admin_name, value: obj[G_variable], hs_value: obj.hs_value } : extreme, G_variable === "women_in_agric" ? { admin_name: null, value: -Infinity } : { admin_name: null, value: Infinity } ); let classified_ac = classifyACValue(heat_HotSpot.ac_value) let classified_hs = classifyHSValue(gender_HotSpot.hs_value) let regionIntro = country === "SSA" ? {en: "", fr: ""} : {en: "", fr: "la region de"} let regionIntro_cap = country === "SSA" ? {en: "", fr: ""} : {en: "", fr: "La region de"} let template_fillers = [ { name: "country", value: _lang(nbText.General.article_adminNames[country]) }, { name: "regionIntro", value: _lang(regionIntro) }, { name: "regionIntro_cap", value: _lang(regionIntro_cap) }, { name: "highest_gender", value: translateA0_only(gender_HotSpot.admin_name)}, { name: "HS_level", value: _lang(nbText.GenderHotspots.dynamicLevels[classified_hs.toLowerCase()])}, { name: "heatstress_var", value: _lang(nbText.GenderHotspots.plot.heatLabels[heat_hazard]).toLowerCase() }, { name: "highest_hs", value: translateA0_only(heat_HotSpot.admin_name)}, { name: "AC_level", value: _lang(nbText.GenderHotspots.dynamicLevels[classified_ac.toLowerCase()])}]; let md_text; let template if (G_variable === "women_in_agric") { template = _lang(nbText.GenderHotspots.dynamicInsight_womenInAgri) } else { template_fillers.push( { name: "gender_var", value: _lang(nbText.GenderHotspots.domains[G_variable]).toLowerCase()} ) template = _lang(nbText.GenderHotspots.dynamicInsight_FEMI) } return md`${Lang.reduceReplaceTemplateItems(template, template_fillers)}` function translateA0_only(adminName) { const translatedName = _lang(nbText.General.article_adminNames[adminName]); return translatedName === undefined ? adminName : translatedName; } }38:T136d,tableHTML = { //Translation solution for these is just have all values in an obj. I.e. table_vals = {Livestock:{}, Reinforce:{}} const columns = [ "Sector", // "Adaptation Options", "Gender Outcome Score", "Geography", "Confidence in Result", "Risk" // "Reference" ]; const table_data = gender_solutions.filter( (d) => d["Adaptation Options"] === adaptation ); let table = ` ${columns .map( (c) => `` ) .join("")}`; for (let [index, row] of table_data.entries()) { let rowColor = index % 2 === 0 ? "#f2f2f2" : "#ffffff"; table += `${columns .map((c) => { if (c === "Gender Outcome Score" && row[c]) { let outcome = row[c]; let sign = ""; let tipText; if (outcome === "Enabling") { sign = `+`; tipText = _lang(nbText.BridgeGap.table.tooltip.enable); } else if (outcome === "Reinforcing") { sign = `+`; tipText = _lang(nbText.BridgeGap.table.tooltip.reinforce); } else if (outcome === "Constraining") { sign = `-`; tipText = _lang(nbText.BridgeGap.table.tooltip.constrain); } else { sign = `-`; tipText = _lang(nbText.BridgeGap.table.tooltip.counteract); } return ``; } else if (c === "Geography" && row[c]) { return ``; } else if (c === "Risk" && row[c]) { return ``; } else if (c === "Confidence in Result" && row[c]) { let agreementColor; if (row[c] === "High") { agreementColor = "green"; } else if (row[c] === "Medium") { agreementColor = "orange"; } else { agreementColor = "red"; } return ``; } else { return ``; } }) .join("")}`; } table += `

${_lang(nbText.BridgeGap.table.headings[c])}
${sign} ${_lang(nbText.BridgeGap.table.values[row[c].toLowerCase()])}	${row[c] .split(",") .map((country) => country_flags[country.trim()] ? `${ country_flags[country.trim()].outerHTML }` : country ) .join(" ")}	${row[c] .split(",") .map((risk) => climate_risk_icons[risk.trim()] ? `${ climate_risk_icons[risk.trim()].outerHTML }` : risk ) .join(" ")}	${_lang(nbText.BridgeGap.table.values[row[c].toLowerCase()])}	${row[c].split(", ").map((v) => _lang(nbText.BridgeGap.table.values[v.toLowerCase()])).join(", ")}

`; let tableContainer = `

${table}

`; return html`${tableContainer}`; }39:Tb44a,nbText = new Object({ toc : { title: {en: "In this notebook", fr: "Dans ce notebook"} }, overview : { title: {en: 'Overview', fr: "Vue d’Ensemble"}, text: { en: `In the agricultural sectors of low- and middle-income nations, particularly within Africa and Asia, climate change acts as a formidable challenge, disproportionately affecting women farmers. Despite their pivotal role, women are notably more susceptible to climate adversities due to structural gender inequalities and social norms that limit their access to resources, land, and decision-making. At the same time, women can be powerful agents of change in enhancing climate resilience- past studies consistently demonstrate that achieving gender equality at household and community levels is directly associated with improved outcomes in agriculture, adaptation and development. How can we better understand this nexus, and where do we channelize investments which increase the agency of women for improved climate and development outcomes?`, fr: `Dans les secteurs agricoles des pays à revenu faible et intermédiaire, en particulier en Afrique et en Asie, le changement climatique constitue un défi redoutable, affectant de manière disproportionnée les agricultrices. Malgré leur rôle crucial, les femmes sont particulièrement vulnérables aux aléas climatiques en raison des inégalités structurelles de genre et des normes sociales qui limitent leur accès aux ressources, à la terre et à la prise de décision. Parallèlement, les femmes peuvent être de puissantes agentes de changement en renforçant la résilience climatique - des études passées montrent de manière constante que la réalisation de l'égalité des genres au niveau des ménages et des communautés est directement associée à de meilleurs résultats en matière d'agriculture, d'adaptation et de développement. Comment mieux comprendre ce lien, et où canaliser les investissements pour accroître le pouvoir des femmes afin d'améliorer les résultats climatiques et de développement? `} }, GenderedAfriAG: { title: {en: 'African Agriculture Is Gendered', fr: "L'agriculture africaine est genrée."}, text: { en: `In Africa, women's participation in agriculture is both extensive and varied, encompassing a wide range of roles from cultivation to market. The experiences, opportunities, and challenges in farming and livestock systems are distinctly different, women are engaged in planting, weeding, harvesting, and processing agricultural products, often taking on the majority of post-harvest activities, including storage, handling, and marketing of crops. The section below highlights how women labor participation varies across different commodities and activity domains.`, fr: `En Afrique, la participation des femmes dans l'agriculture est à la fois vaste et variée, couvrant un large éventail de rôles allant de la culture au marché. Les expériences, opportunités et défis dans les systèmes agricoles et d'élevage sont distinctement différents. Les femmes sont impliquées dans la plantation, le désherbage, la récolte et la transformation des produits agricoles, assumant souvent la majorité des activités post-récolte, y compris le stockage, la manutention et la commercialisation des cultures. La section ci-dessous met en évidence comment la participation des femmes au travail varie selon les différentes commodités et domaines d'activité.` }, LegendScale: { high: {en: "High", fr: "Élevée"}, low: {en: "Low", fr: "Faible"} }, FemaleParticipationLabor: { title: {en: "Female Participation In Agricultural Labor", fr: 'Participation féminine dans le travail agricole'}, plot: { practices : { "Land Prep." : {en: "Land Prep.", fr: "Préparation des terres"}, "Planting/Weeding": {en: "Planting/Weeding", fr: "Plantation/Désherbage"}, Harvesting: {en: "Harvesting", fr: "Récolte"} } } }, WomenDominate: { title: {en: "Women Dominate Certain Crops", fr: "Les femmes dominent certaines cultures"}, text: { en: `Men predominantly engage in growing cash crops, whereas women focus more on staple and food crops. This division varies across regions, with the female contribution to non-edible crop labor differing notably between countries like Malawi, Uganda, Tanzania, and Nigeria, illustrating these trends are not universally consistent across different geographies.`, fr: `Les hommes se consacrent principalement à la culture des cultures de rente, tandis que les femmes se concentrent davantage sur les cultures vivrières et alimentaires. Cette division varie selon les régions, avec une contribution féminine au travail des cultures non comestibles différant notablement entre des pays comme le Malawi, l'Ouganda, la Tanzanie et le Nigeria, illustrant que ces tendances ne sont pas uniformément constantes à travers différentes géographies.` }, headline: { default: {en:"Across these 6 countries, Women are most involved with the production of **Roots and Tubers** at **42%**. This is worth an estimated **Int$11B**.", fr:"Dans ces six pays, les femmes sont le plus impliquées dans la production de racines et **tubercules à hauteur** de **42%**. Cela représente une valeur estimée à **11 milliards de dollars internationaux.**"}, valid_hover: { en: `In **:::country:::**, women are **:::involvement:::** involved with **:::cropType:::**, compared to other crops. Women contribute **:::cropPCT:::%** to production of **:::cropType:::**, worth **ind:::cropValue:::**.`, fr: `Dans **:::country:::**, les femmes sont **:::involvement:::** impliquées dans la production de **:::cropType:::** par rapport à d'autres cultures. Les femmes contribuent à hauteur de **:::cropPCT:::%** à la production de **:::cropType:::**, représentant une valeur de **:::cropValue:::** dollars internationaux` }, null_hover: { en: `There is no data for women involvement in **:::country:::** for **:::cropType:::**`, fr: `Il n'y a pas de données sur l'implication des femmes dans **:::country:::** pour **:::cropType:::**` } } }, FemaleParticipationCrops: { title: {en: "Female Participation In The Production Of Crops", fr: 'Participation féminine dans la production de cultures'}, plot: { crops: { "Cereals" : {en: "Cereals", fr: "Céréales"}, "Legumes" : {en: "Legumes", fr: "Légumineuses"}, "Roots & Tubers" : {en: "Roots & Tubers", fr: "Racines et tubercules"}, "Fruits & Vegetables" : {en: "Fruits & Vegetables", fr: "Fruits et légumes"}, "Non-edible crops" : {en: "Non-edible crops", fr: "Cultures non comestibles"} } } }, }, GenderInvestments: { title: {en: "Need For Gender-Inclusive Investments", fr: "Nécessité d'investissements inclusifs en matière de genre"}, text: { en: `The adaptive capacity of women in agriculture varies significantly due to factors such as socio-economic status, education, access to resources, and systemic barriers. Female empowerment and wellbeing metrics reveal the disparities women face in areas such as employment, income, health, and leadership. These indicators highlight the multifaceted nature of women's vulnerability, demonstrating variations in their access to opportunities, resources, and autonomy.`, fr: `La capacité d'adaptation des femmes dans l'agriculture varie considérablement en raison de facteurs tels que le statut socio-économique, l'éducation, l'accès aux ressources et les barrières systémiques. Les indicateurs d'autonomisation et de bien-être des femmes révèlent les disparités auxquelles elles sont confrontées dans des domaines tels que l'emploi, le revenu, la santé et le leadership. Ces indicateurs mettent en évidence la nature multifacette de la vulnérabilité des femmes, démontrant les variations dans leur accès aux opportunités, aux ressources et à l'autonomie.` }, GenderIndex: { title: {en: "Average Index Scores Across Female Empowerment Domains", fr: "Scores moyens des indices dans les domaines de l'autonomisation des femmes"}, plot: { title_X: {en : "Female Well-being and Empowerment (0-1)", fr: "Bien-être et autonomisation des femmes (0-1)"}, domains: { "empowerment_index" : {en: "Empowerment Index", fr: "Indice d'autonomisation"}, "decisions" : {en: "Decision Making", fr: "Prise de décision"}, "domestic_violence" : {en: "Domestic Safety", fr: "Sécurité Domestique"}, "education" : {en: "Education", fr: "Éducation"}, "employment" : {en: "Employment", fr: "Emploi"}, "family_planning" : {en: "Family Planning", fr: "Planification familiale"}, "reproductive_health" : {en:"Reproductive Health", fr: "Santé reproductive"} } } } }, GenderHotspots: { title: {en: "Gendered Climate Hotspots", fr: "Zones critiques climatiques genrées"}, text: { en: `Climate change can act as a risk amplifier and further exacerbate the gendered differences and outcomes for women in agriculture. The map below shows the intersection of projected risk from heat stress and exposure based on women participation in agriculture, showing critical hotspots. It can also be used to explore the intersection of risk from heat stress and variables related to female well-being and empowerment, identifying areas where existing gender disparities amplify the risk. These results highlight the need for targeted interventions especially in the hotspot areas, to support women in agriculture, and help them adapt to climate risk.`, fr: `Le changement climatique peut agir comme un amplificateur de risques et exacerber davantage les différences et les résultats liés au genre pour les femmes dans l'agriculture. La carte ci-dessous montre l'intersection des risques projetés de stress thermique et de l'exposition en fonction de la participation des femmes à l'agriculture, mettant en évidence les zones critiques. Elle peut également être utilisée pour explorer l'intersection des risques de stress thermique et des variables liées au bien-être et à l'autonomisation des femmes, identifiant ainsi les zones où les disparités de genre existantes amplifient le risque. Ces résultats soulignent la nécessité d'interventions ciblées, en particulier dans les zones critiques, pour soutenir les femmes dans l'agriculture et les aider à s'adapter aux risques climatiques.`}, selectors: { heatHazard: { title : {en: "Heat Hazard", fr: "Risque de Chaleur"}, hazards: { human: {en : "Human", fr: "Humain"}, crop: {en : "Crop", fr: "Cultures"}, livestock: {en : "Livestock", fr: "Bétail"}, } }, GenderVar: { title : {en: "Gender Variable", fr: "Variable de Genre"}, domains: { "women_in_agric" : {en: "Women Involved in Agriculture", fr: "Nombre de femmes impliquées dans l'agriculture"}, "empowerment_index" : {en: "Empowerment Index", fr: "Indice d'autonomisation"}, "decisions" : {en: "Decision Making", fr: "Prise de décision"}, "domestic_violence" : {en: "Domestic Safety", fr: "Sécurité Domestique"}, "education" : {en: "Education", fr: "Éducation"}, "employment" : {en: "Employment", fr: "Emploi"}, "family_planning" : {en: "Family Planning", fr: "Planification familiale"}, "reproductive_health" : {en:"Reproductive Health", fr: "Santé reproductive"} } } }, plot : { heatLabels: { human: {en: "Human Heat Stress", fr: "Stress thermique humain"}, crop: {en: "Crop Heat Stress", fr: "Stress thermique des cultures"}, livestock: {en: "Livestock Heat Stress", fr: "Stress thermique du bétail"}, } }, dynamicInsight_womenInAgri: { en: `Across **:::country:::**, **:::highest_gender:::** has the **highest** number of women working in agriculture and **:::HS_level:::** levels of :::heatstress_var:::. **:::highest_hs:::** has the **highest** levels of :::heatstress_var::: and **:::AC_level:::** number of women working in agriculture. These may be areas where women face the greatest risk from climate change.`, fr: `À travers **:::country:::**, :::regionIntro::: **:::highest_gender:::** a le plus **grand** nombre de femmes dans l'agriculture et présente des niveaux **:::HS_level:::** de :::heatstress_var:::. :::regionIntro_cap::: **:::highest_hs:::** a les niveaux les plus élevés de :::heatstress_var::: et un nombre **:::AC_level:::** de femmes dans l'agriculture. Ce sont peut-être des zones où les femmes sont les plus exposées aux risques du changement climatique.` }, dynamicInsight_FEMI: { en: `Across **:::country:::**, **:::highest_gender:::** has the **lowest** levels of :::gender_var::: for women and **:::HS_level:::** levels of :::heatstress_var:::. **:::highest_hs:::** has the **highest** levels of **:::heatstress_var:::** and **:::AC_level:::** levels of :::gender_var::: for women. These may be areas where women have a lower ability to adapt to climate heat stresses.`, fr: `À travers **:::country:::**, :::regionIntro::: **:::highest_gender:::** a le plus **faible** niveau :::gender_var::: et des niveaux **:::HS_level:::** de :::heatstress_var:::. :::regionIntro_cap::: **:::highest_hs:::** a les niveaux les plus élevés de :::heatstress_var::: et des niveaux **:::AC_level:::** :::gender_var:::. Ce sont peut-être des zones où les femmes sont les plus exposées aux risques du changement climatique.` }, dynamicLevels: { low: {en: "low", fr: "faible"}, moderate: {en: "moderate", fr: "modérés"}, high: {en: "high", fr: "élevé"}, }, domains: { "women_in_agric" : {en: "women involved in agriculture", fr: "nombre de femmes impliquées dans l'agriculture"}, "empowerment_index" : {en: "empowerment Index", fr: "d'indice d'autonomisation"}, "decisions" : {en: "decision making", fr: "de prise de décision"}, "domestic_violence" : {en: "domestic safety", fr: "de sécurité Domestique"}, "education" : {en: "education", fr: "d'éducation"}, "employment" : {en: "employment", fr: "d'emploi"}, "family_planning" : {en: "family planning", fr: "de planification familiale"}, "reproductive_health" : {en:"reproductive health", fr: "de santé reproductive"} } }, BridgeGap: { title: {en: "Bridging The Gender Gap: From Hotspots To Solutions", fr: "Combler l'écart entre les genres: des zones critiques aux solutions"}, text: { en: `Climate-focused adaptation strategies, by including gender-responsive approaches, can significantly enhance women's empowerment and hasten the progress toward achieving broader developmental goals. These interventions can not only foster gender equality but also catalyze sustainable development across multiple dimensions, ensuring that climate actions are both inclusive and equitable. The table below provides evidence on how adaptation solutions inform gender outcomes.`, fr: `Les stratégies d'adaptation axées sur le climat, en incluant des approches sensibles au genre, peuvent considérablement renforcer l'autonomisation des femmes et accélérer le progrès vers la réalisation d'objectifs de développement plus larges. Ces interventions peuvent non seulement favoriser l'égalité des genres, mais aussi catalyser un développement durable dans plusieurs dimensions, garantissant que les actions climatiques soient à la fois inclusives et équitables. Le tableau ci-dessous fournit des preuves sur la manière dont les solutions d'adaptation influencent les résultats en matière de genre.` }, selector: { title: {en: "Adaptation Option", fr: "Option d'adaptation"}, options: { "Financial mechanisms and knowldege management": { en: "Financial mechanisms and knowldege management", fr: "Mécanismes financiers et gestion des connaissances" }, "Migration": {en: "Migration", fr: "Migration"}, "Natural resource management": { en: "Natural resource management", fr: "Gestion des ressources naturelles"}, "Biodiversity conversation": {en: "Biodiversity conversation", fr: "Conservation de la biodiversité"}, "Livelihood diversification": { en: "Livelihood diversification", fr: "Diversification des moyens de subsistance"}, } }, table: { headings : { "Sector": {en: "Sector", fr: "Secteur"}, "Gender Outcome Score" : {en: "Gender Outcome Score", fr: "Le Score de Résultat en Matière de Genre"}, "Geography": {en: "Geography", fr: "La Géographie"}, "Confidence in Result" : {en: "Confidence in Result", fr: "Confiance dans le résultat"}, "Risk": {en: "Risk", fr: "Risque"} }, values :{ low: {en: "Low", fr: "Faible"}, medium: {en: "Moderate", fr: "Modéré"}, high: {en: "High", fr: "Élevé"}, crop: {en: "Crop", fr: "Cultures"}, livestock: {en: "Livestock", fr: "Bétail"}, agriculture: {en: "Agriculture", fr: "Agriculture"}, enabling: {en: "Enabling", fr: "Facilitant"}, counteracting: {en: "Counteracting", fr: "Contrebalançant"}, reinforcing: {en: "Reinforcing", fr: "Renforçant"}, constraining: {en: "Constricting", fr: "Contraignant"}, }, tooltip: { enable: {en: "Creates conditions which further a gender target", fr: "Crée des conditions qui favorise un objectif de genre"}, reinforce: {en: "Aids the achievement of gender targets", fr: "Aide à atteindre les objectifs de genre"}, constrain: {en: "Limits options to the success of a gender target", fr: "Limite les options au succès d'un objectif de genre"}, counteract: {en: "Inhibits the achievement of gender targets", fr: "Inhibe l'atteinte des objectifs de genre"}, "climate change": {en: "Climate Change", fr:"Changement Climatique"}, "drought": {en: "Drought", fr:"Sécheresse"}, "heat stress": {en: "Heat Stress", fr:"Stress Thermique"}, "extreme weather events": {en: "Extreme Weather Events", fr:"Événements Climaiques Extrêmes"}, "rainfall variability": {en: "Rainfall Variability", fr:"Variabilité des précipitations"}, "multiple risks": {en: "Multiple Risks", fr:"Risques Multiples"}, "flood": {en: "Flood", fr:"Innondation"}, } } }, Summary: { title: {en : "Summary", fr: "Résumé"}, text: { en: `Gender-inclusive agricultural investments are essential for overcoming existing inequalities and mitigating future climate risks. Achieving gender equality and women’s empowerment is integral to the success of all 17 Sustainable Development Goals (SDGs). Our analysis shows that women are differentially involved in different crops and geographies, and it highlights the regions where investments may be needed most. These climate investments should focus on driving inclusive adaptation pathways to ensure a sustainable future for all.`, fr: `Les investissements agricoles inclusifs en matière de genre sont essentiels pour surmonter les inégalités existantes et atténuer les futurs risques climatiques. Atteindre l'égalité des genres et l'autonomisation des femmes est indispensable au succès des 17 Objectifs de Développement Durable (ODD). Notre analyse montre que les femmes sont différemment impliquées dans diverses cultures et géographies, et elle met en lumière les régions où les investissements peuvent être les plus nécessaires. Ces investissements climatiques devraient se concentrer sur la promotion de voies d'adaptation inclusives pour assurer un avenir durable pour tous.` } }, MethodsSources: { title: {en: "Methods & Sources", fr: "Méthodes et sources"}, datasets : { title: {en: "Datasets", fr: "Jeux de données"}, femaleParticipation:{ title: {en: "Female participation in agricultural labor and crop production", fr: "Participation féminine dans le travail agricole et la production de cultures"}, text:{ en: ` - The data for female labor participation in various agricultural activities and production of different crops across multiple countries is taken from [Palacios-Lopez, A., Christiaensen, L. and Kilic, T. (2017)](https://doi.org/10.1016/j.foodpol.2016.09.017). The data is based on a survey of ~2000 households in 6 countries circa 2015. Data for North and South Nigeria was combined by taking the mean value for each crop or agricultural activity. - **[Crop value of production](https://radiantearth.github.io/stac-browser/#/external/digital-atlas.s3.amazonaws.com/stac/public_stac/exposure_catalog/mapspam2017/mapspam2017_vop_parquet/mapspam2017_vop_parquet.json)** is from MapSPAM 2017 V2r3 (Spatial Production Allocation Model) where production values are multiplied by country specific FAOstat international crop prices in 2005 International Dollars. A 2005 [international dollar](https://en.wikipedia.org/wiki/International_dollar) is a hypothetical unit of currency that has the same purchasing power parity as the U.S. dollar had in the United States in 2005, allowing for comparison of what consumers can buy in different countries with the same amount of money. This dataset is an updated version of MapSPAM specifically tailored for this project. It includes bug fixes and incorporates country-specific values of production instead of global averages.`, fr: ` - Les données sur la participation féminine aux diverses activités agricoles et à la production de différentes cultures dans plusieurs pays proviennent de l'étude de [Palacios-Lopez, A., Christiaensen, L. et Kilic, T. (2017)](https://doi.org/10.1016/j.foodpol.2016.09.017). Ces données sont basées sur une enquête auprès d'environ 2000 ménages dans 6 pays vers 2015. Les données pour le nord et le sud du Nigeria ont été combinées en prenant la valeur moyenne pour chaque culture ou activité agricole. - **[La valeur de la production des cultures](https://radiantearth.github.io/stac-browser/#/external/digital-atlas.s3.amazonaws.com/stac/public_stac/exposure_catalog/mapspam2017/mapspam2017_vop_parquet/mapspam2017_vop_parquet.json?.language=fr)** provient de MapSPAM 2017 V2r3 (Spatial Production Allocation Model) où les valeurs de production sont multipliées par les prix internationaux spécifiques aux cultures de chaque pays fournis par FAOstat en dollars internationaux de 2005. Un [dollar international](https://fr.wikipedia.org/wiki/Dollar_Geary-Khamis) de 2005 est une unité hypothétique de monnaie ayant la même parité de pouvoir d'achat que le dollar américain aux États-Unis en 2005, permettant de comparer ce que les consommateurs peuvent acheter dans différents pays avec le même montant d'argent. Ce jeu de données est une version mise à jour de MapSPAM spécialement adaptée pour ce projet. Il inclut des corrections de bugs et incorpore des valeurs de production spécifiques à chaque pays au lieu de moyennes mondiales.` } }, heatStress:{ title: {en: "Heat Stress for Human, Crops, and Livestock", fr: "Stress thermique pour les humains, les cultures et le bétail"}, text:{ en: ` The Heat Stress Data is a subset of a [climate hazard dataset](https://radiantearth.github.io/stac-browser/#/external/digital-atlas.s3.amazonaws.com/stac/public_stac/hazard_catalog/hazard_timeseries_mean_annual/hazard_timeseries_mean_pq_annual/hazard_timeseries_mean_pq_annual.json?.language=fr), detailed [here](https://observablehq.com/d/d8c0692154e6c87e?collection=@adaptationatlas/data-spotlights#methods-sources), which includes climate hazard averages for each administrative boundary, across the SSPs and time periods. Historical heat stress refers to the period 1995-2014 (aligned with IPCC AR 6), while future heat stress utilizes the CMIP 6 ensemble average for the 2050s (2041-2060). - **Human heat stress** is based on heat index equations from Steadman ([1979a](https://doi.org/10.1175/1520-0450%281979%29018%3C0861:TAOSPI%3E2.0.CO;2); [1979b](https://doi.org/10.1175/1520-0450%281979%29018%3C0874:TAOSPI%3E2.0.CO;2)) and uses a combination of **mean** air temperature (dry-bulb) and relative humidity. - **Crop heat stress** is based on heat stress for maize. It is defined as the number of days with daily **maximum** temperatures (dry-bulb) above a given threshold of 35ºC during the flowering period. The growing season is based on the maize crop calendar from [Sacks et al. (2010)](https://sage.nelson.wisc.edu/data-and-models/datasets/crop-calendar-dataset/). - **Livestock heat stress** is calculated using the Thermal Humidity Index for cattle from [Rahimi et al. (2020)](https://doi.org/10.1007/s10584-020-02733-2). It uses the **maximum** air temperature (dry-bulb) and relative humidity. More details and the equations used can be found [here](https://github.com/AdaptationAtlas/hazards/wiki/Hazards-definitions).`, fr: ` Les données sur le stress thermique sont un sous-ensemble d'un jeu [de données sur les aléas climatiques](https://observablehq.com/d/d8c0692154e6c87e?collection=@adaptationatlas/data-spotlights#methods-sources), détaillé [ici](https://observablehq.com/d/d8c0692154e6c87e?collection=@adaptationatlas/data-spotlights#methods-sources), qui comprend les moyennes des aléas climatiques pour chaque limite administrative, à travers les SSP (Shared Socioeconomic Pathways) et les périodes temporelles. Le stress thermique historique se réfère à la période 1995-2014 (alignée avec le rapport AR6 du GIEC), tandis que le stress thermique futur utilise la moyenne de l'ensemble CMIP6 pour les années 2050 (2041-2060). - **Le stress thermique humain** est basé sur les équations de l'indice de chaleur de Steadman ([1979a](https://doi.org/10.1175/1520-0450%281979%29018%3C0861:TAOSPI%3E2.0.CO;2); [1979b](https://doi.org/10.1175/1520-0450%281979%29018%3C0874:TAOSPI%3E2.0.CO;2)) et utilise une combinaison de la température **moyenne** de l'air (à bulbe sec) et de l'humidité relative. - **Le stress thermique des cultures** est basé sur le stress thermique pour le maïs. Il est défini comme le nombre de jours avec des températures **maximales** quotidiennes (à bulbe sec) supérieures à un seuil donné de 35ºC pendant la période de floraison. La saison de croissance est basée sur le calendrier des cultures de maïs de [Sacks et al. (2010)](https://sage.nelson.wisc.edu/data-and-models/datasets/crop-calendar-dataset/). - Le stress thermique du bétail est calculé en utilisant l'indice thermique d'humidité pour le bétail de [Rahimi et al. (2020)](https://doi.org/10.1007/s10584-020-02733-2). Il utilise la température **maximale** de l'air (à bulbe sec) et l'humidité relative. Plus de détails et les équations utilisées peuvent être trouvés [ici](https://github.com/AdaptationAtlas/hazards/wiki/Hazards-definitions).` } }, FemaleWellbeing: { title: {en: 'Female Well-being and Empowerment data', fr: "Données sur le bien-être et l'autonomisation des femmes"}, text: { en: `The [Female Empowerment Index](https://radiantearth.github.io/stac-browser/#/external/digital-atlas.s3.amazonaws.com/stac/public_stac/adaptive-capacity/women-and-gender/female-empowerment/collection.json) combines data on domestic violence, employment, reproductive healthcare, decision-making power, and family planning. The index, and the variables it is derived from, are from [Rettig, Erica (2022)](https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/8GJKYW) and are build using data from [DHS Program](https://dhsprogram.com). The individual variables and the Empowerment Index are based on the 2015 calculations. All of these have been normalized between 0 and 1, where 0 represents lowest empowerment and 1 represents highest empowerment`, fr: `[L'Indice d'Autonomisation des Femmes](https://radiantearth.github.io/stac-browser/#/external/digital-atlas.s3.amazonaws.com/stac/public_stac/adaptive-capacity/women-and-gender/female-empowerment/collection.json?.language=fr) combine des données sur la violence domestique, l'emploi, les soins de santé reproductive, le pouvoir de décision et la planification familiale. L'indice, et les variables dont il est dérivé, proviennent de [Rettig, Erica (2022)](https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/8GJKYW) et sont construits à partir des données du [programme DHS](https://dhsprogram.com). Les variables individuelles et l'Indice d'Autonomisation sont basés sur les calculs de 2015. Tous ces éléments ont été normalisés entre 0 et 1, où 0 représente le plus faible niveau d'autonomisation et 1 le plus élevé.` } }, WomenInAg: { title: {en: "Number of Women Involved In Agriculture", fr: "Nombre de femmes impliquées dans l'agriculture"}, text: { en: ` - Female population data is based on the WorldPop age/sex structure dataset, and includes all females aged 65 or younger [(WorldPop, 2016)](https://hub.worldpop.org/geodata/summary?id=1276). This data has a spatial resolution of 1 km and is based on the WorldPop 2015 estimates. - Percentage of women working in agriculture per admin level 1 boundary is taken from the LivWell dataset [(Belmin et al., 2022)](https://doi.org/10.1038/s41597-022-01824-2). In regions not covered by the Livwell data, country scale data from the ILO stat modeled estimate of employment in agriculture is used [(International Labour Organization, 2022)](https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/).`, fr: ` - Les données sur la population féminine sont basées sur le jeu de données sur la structure par âge/sexe de WorldPop, et incluent toutes les femmes âgées de 65 ans ou moins [(WorldPop, 2016)](https://hub.worldpop.org/geodata/summary?id=1276). Ces données ont une résolution spatiale de 1 km et sont basées sur les estimations de WorldPop pour 2015. - Le pourcentage de femmes travaillant dans l'agriculture par limite administrative de niveau 1 est tiré du jeu de données LivWell [(Belmin et al., 2022)](https://doi.org/10.1038/s41597-022-01824-2). Dans les régions non couvertes par les données LivWell, les données à l'échelle nationale de l'estimation modélisée de l'emploi dans l'agriculture de l'ILO stat sont utilisées [(International Labour Organization, 2022)](https://ilostat.ilo.org/fr/methods/concepts-and-definitions/ilo-modelled-estimates/).` } }, adaptationSolutions:{ title: {en: "Adaptation Solutions and Gender Outcomes", fr: "Solutions d'adaptation et résultats en matière de genre"}, text: { en: `The solution data is based on a systematic review of adaptation solutions and evidence on their gender outcomes by [Roy, J., Prakash, A., Some, S. et al. (2022)](https://doi.org/10.1057/s41599-022-01266-6). This review is comprised of >17,000 studies on gender and climate adaptation globally. `, fr: `Les données sur les solutions sont basées sur une revue systématique des solutions d'adaptation et des preuves de leurs résultats en matière de genre réalisée par [Roy, J., Prakash, A., Some, S. et al. (2022)](https://doi.org/10.1057/s41599-022-01266-6). Cette revue comprend plus de 17 000 études sur le genre et l'adaptation au climat à l'échelle mondiale.` } }, boundaries:{ title: {en: "Boundaries", fr: "Délimitations"}, text: { en: `[Administrative areas](https://radiantearth.github.io/stac-browser/#/external/digital-atlas.s3.amazonaws.com/stac/public_stac/boundary_catalog/geoBoundaries_SSA/collection.json) used in this notebook come from [geoBoundaries 6.0.0](https://github.com/wmgeolab/geoBoundaries). The gbHumanitarian boundaries are used and if not available then the gbOpen boundaries are substituted.`, fr: `[Les zones administratives](https://radiantearth.github.io/stac-browser/#/external/digital-atlas.s3.amazonaws.com/stac/public_stac/boundary_catalog/geoBoundaries_SSA/collection.json?.language=fr) utilisées dans ce notebook proviennent de [geoBoundaries 6.0.0](https://github.com/wmgeolab/geoBoundaries). Les frontières gbHumanitarian sont utilisées et, si elles ne sont pas disponibles, les frontières gbOpen sont substituées.` } } }, methods: { title: {en: "Methods", fr: "Méthodologie"}, WomenInAg: { title: {en: "Number of Women in Agriculture", fr: "Nombre de femmes dans l'agriculture"}, text: { en: `The female population data was multiplied by the estimated percentage of women involved with agriculture for each respective region. We then extracted this data by admin 1 boundaries to create the final dataset showing the total number of women working in agriculture within each country and admin 1 region.`, fr: `Les données sur la population féminine ont été multipliées par le pourcentage estimé de femmes impliquées dans l'agriculture pour chaque région respective. Nous avons ensuite extrait ces données par limites administratives de niveau 1 pour créer le jeu de données final montrant le nombre total de femmes travaillant dans l'agriculture dans chaque pays et région administrative de niveau 1.` } }, hotspots : { title: {en: "Heat Stress and Gender Hot Spots", fr: "Stress thermique et zones critiques genrées"}, text: { en: ` Human, crop, and livestock heat stress and the female empowerment datasets were extracted to admin boundaries to get the average value for each region. Following this, we classified the heat stress data according to the following severity thresholds: | Category | Low Threshold | Moderate Threshold | High Threshold | |---|---|---|---| | Human | <27 | 27-41 | >41 | | Crop | <9 | 9-25 | >25 | | Livestock | <72| 72-90 | >90 | The number of Women involved in agriculture, along with the female empowerment and well-being variables were classified according to the tertile distribution of the datasets. We then overlaid these two classified datasets to highlight regions of high heat stress and high numbers of women involved in agriculture and areas with high heat stress and low female empowerment and well-being. `, fr: ` Le stress thermique pour les humains, les cultures et le bétail, ainsi que les jeux de données sur l'autonomisation des femmes, ont été extraits aux limites administratives pour obtenir la valeur moyenne de chaque région. Ensuite, nous avons classé les données sur le stress thermique selon les seuils de gravité suivants: | Catégorie | Seuil faible | Seuil modéré | Seuil élevé | |---|---|---|---| | Humain | <27 | 27-41 | >41 | | Culture | <9 | 9-25 | >25 | | Bétail | <72| 72-90 | >90 | Le nombre de femmes impliquées dans l'agriculture, ainsi que les variables d'autonomisation et de bien-être des femmes, ont été classés selon la distribution en tertiles des jeux de données. Nous avons ensuite superposé ces deux jeux de données classifiés pour mettre en évidence les régions de fort stress thermique et de fort nombre de femmes impliquées dans l'agriculture, ainsi que les zones de fort stress thermique et de faible autonomisation et bien-être des femmes.` } }, solutions: { title: {en: "Adaptation Solutions", fr: "Solutions d'adaptation"}, text: { en: `The solutions data was subset by studies that were focused on agriculture and located in Africa. The resulting adaptive solutions were grouped in four key categories: - Financial mechanisms and knowledge management (including studies on insurance, credit, and microfinance) - Migration - Natural resource management - Biodiversity conservation - Livelihood diversification The adaptation category, intervention, gender outcome score (SDG 5 score), geography, risk, and degree of agreement from each of these studies was extracted for inclusion in the table.`, fr: `Les données sur les solutions ont été extraites d'études centrées sur l'agriculture et situées en Afrique. Les solutions d'adaptation résultantes ont été regroupées en quatre catégories clés: - Mécanismes financiers et gestion des connaissances (y compris les études sur l'assurance, le crédit et la microfinance) - Migration - Gestion des ressources naturelles - Conservation de la biodiversité - Diversification des moyens de subsistance La catégorie d'adaptation, l'intervention, le score de résultat en matière de genre (score ODD 5), la géographie, le risque et le degré d'accord de chacune de ces études ont été extraits pour inclusion dans le tableau.` } } } }, Appendix: { title: {en: "Appendix", fr: "Annexe"} }, General: { low: {en: "Low", fr: "Faible"}, moderate: {en: "Moderate", fr: "Modéré"}, high: {en: "High", fr: "Élevé"}, more: {en: "more", fr: "plus"}, less: {en: "less", fr: "moins"}, mean: {en: "Average", fr: "moyenne"}, regional_avg: {en: "Regional Average", fr: "Moyenne Régionale" }, national_avg: {en: "Country Average", fr: "Moyenne Nationale" }, "country" : {en: "Country", fr: "Pays"}, "region": {en: "Region", fr: "Région"}, "scenario" : {en: "Scenario", fr: "Scénario"}, 'historic' : {en: "Historic", fr: "Historique"}, noDataAvailable : {en: "No Data Available", fr: "Aucune donnée disponible"}, adminNames: { "Full Country" : {en: "Full Country", fr: "Pays Entier"}, "Angola": { "en": "Angola", "fr": "Angola" }, "Benin": { "en": "Benin", "fr": "Bénin" }, "Botswana": { "en": "Botswana", "fr": "Botswana" }, "Burkina Faso": { "en": "Burkina Faso", "fr": "Burkina Faso" }, "Burundi": { "en": "Burundi", "fr": "Burundi" }, "Cameroon": { "en": "Cameroon", "fr": "Cameroun" }, "Central African Republic": { "en": "Central African Republic", "fr": "République Centrafricaine" }, "Chad": { "en": "Chad", "fr": "Tchad" }, "Congo - Brazzaville": { "en": "Congo - Brazzaville", "fr": "Congo - Brazzaville" }, "Congo - Kinshasa": { "en": "Congo - Kinshasa", "fr": "Congo - Kinshasa" }, "Côte d’Ivoire": { "en": "Côte d’Ivoire", "fr": "Côte d’Ivoire" }, "Djibouti": { "en": "Djibouti", "fr": "Djibouti" }, "Equatorial Guinea": { "en": "Equatorial Guinea", "fr": "Guinée Équatoriale" }, "Eritrea": { "en": "Eritrea", "fr": "Érythrée" }, "Eswatini": { "en": "Eswatini", "fr": "Eswatini" }, "Ethiopia": { "en": "Ethiopia", "fr": "Éthiopie" }, "Gabon": { "en": "Gabon", "fr": "Gabon" }, "Gambia": { "en": "Gambia", "fr": "Gambie" }, "Ghana": { "en": "Ghana", "fr": "Ghana" }, "Guinea": { "en": "Guinea", "fr": "Guinée" }, "Guinea-Bissau": { "en": "Guinea-Bissau", "fr": "Guinée-Bissau" }, "Kenya": { "en": "Kenya", "fr": "Kenya" }, "Lesotho": { "en": "Lesotho", "fr": "Lesotho" }, "Liberia": { "en": "Liberia", "fr": "Libéria" }, "Madagascar": { "en": "Madagascar", "fr": "Madagascar" }, "Malawi": { "en": "Malawi", "fr": "Malawi" }, "Mali": { "en": "Mali", "fr": "Mali" }, "Mauritania": { "en": "Mauritania", "fr": "Mauritanie" }, "Mozambique": { "en": "Mozambique", "fr": "Mozambique" }, "Namibia": { "en": "Namibia", "fr": "Namibie" }, "Niger": { "en": "Niger", "fr": "Niger" }, "Nigeria": { "en": "Nigeria", "fr": "Nigéria" }, "Rwanda": { "en": "Rwanda", "fr": "Rwanda" }, "Senegal": { "en": "Senegal", "fr": "Sénégal" }, "Sierra Leone": { "en": "Sierra Leone", "fr": "Sierra Leone" }, "Somalia": { "en": "Somalia", "fr": "Somalie" }, "South Africa": { "en": "South Africa", "fr": "Afrique du Sud" }, "South Sudan": { "en": "South Sudan", "fr": "Soudan du Sud" }, "Sudan": { "en": "Sudan", "fr": "Soudan" }, "Tanzania": { "en": "Tanzania", "fr": "Tanzanie" }, "Togo": { "en": "Togo", "fr": "Togo" }, "Uganda": { "en": "Uganda", "fr": "Ouganda" }, "Zambia": { "en": "Zambia", "fr": "Zambie" }, "Zimbabwe": { "en": "Zimbabwe", "fr": "Zimbabwe" }, "SSA": { "en": "SSA", "fr": "Afrique Subsaharienne" }, "Sub-Saharan Africa": { "en": "Sub-Saharan Africa", "fr": "Afrique Subsaharienne" }, }, article_adminNames: { "Full Country" : {en: "Full Country", fr: "Pays Entier"}, "Angola": { "en": "Angola", "fr": "L’Angola" }, "Benin": { "en": "Benin", "fr": "Le Bénin" }, "Botswana": { "en": "Botswana", "fr": "Le Botswana" }, "Burkina Faso": { "en": "Burkina Faso", "fr": "Le Burkina Faso" }, "Burundi": { "en": "Burundi", "fr": "Le Burundi" }, "Cameroon": { "en": "Cameroon", "fr": "Le Cameroun" }, "Central African Republic": { "en": "Central African Republic", "fr": "La République Centrafricaine" }, "Chad": { "en": "Chad", "fr": "Le Tchad" }, "Congo - Brazzaville": { "en": "Congo - Brazzaville", "fr": "Le Congo - Brazzaville" }, "Congo - Kinshasa": { "en": "Congo - Kinshasa", "fr": "Le Congo - Kinshasa" }, "Côte d’Ivoire": { "en": "Côte d’Ivoire", "fr": "La Côte d’Ivoire" }, "Djibouti": { "en": "Djibouti", "fr": "Djibouti" }, "Equatorial Guinea": { "en": "Equatorial Guinea", "fr": "La Guinée Équatoriale" }, "Eritrea": { "en": "Eritrea", "fr": "L’Érythrée" }, "Eswatini": { "en": "Eswatini", "fr": "L’Eswatini" }, "Ethiopia": { "en": "Ethiopia", "fr": "L’Éthiopie" }, "Gabon": { "en": "Gabon", "fr": "Le Gabon" }, "Gambia": { "en": "Gambia", "fr": "La Gambie" }, "Ghana": { "en": "Ghana", "fr": "Le Ghana" }, "Guinea": { "en": "Guinea", "fr": "La Guinée" }, "Guinea-Bissau": { "en": "Guinea-Bissau", "fr": "La Guinée-Bissau" }, "Kenya": { "en": "Kenya", "fr": "Le Kenya" }, "Lesotho": { "en": "Lesotho", "fr": "Le Lesotho" }, "Liberia": { "en": "Liberia", "fr": "Le Libéria" }, "Madagascar": { "en": "Madagascar", "fr": "Madagascar" }, "Malawi": { "en": "Malawi", "fr": "Le Malawi" }, "Mali": { "en": "Mali", "fr": "Le Mali" }, "Mauritania": { "en": "Mauritania", "fr": "La Mauritanie" }, "Mozambique": { "en": "Mozambique", "fr": "Le Mozambique" }, "Namibia": { "en": "Namibia", "fr": "La Namibie" }, "Niger": { "en": "Niger", "fr": "Le Niger" }, "Nigeria": { "en": "Nigeria", "fr": "Le Nigéria" }, "Rwanda": { "en": "Rwanda", "fr": "Le Rwanda" }, "Senegal": { "en": "Senegal", "fr": "Le Sénégal" }, "Sierra Leone": { "en": "Sierra Leone", "fr": "Sierra Leone" }, "Somalia": { "en": "Somalia", "fr": "La Somalie" }, "South Africa": { "en": "South Africa", "fr": "L’Afrique du Sud" }, "South Sudan": { "en": "South Sudan", "fr": "Le Soudan du Sud" }, "Sudan": { "en": "Sudan", "fr": "Le Soudan" }, "Tanzania": { "en": "Tanzania", "fr": "La Tanzanie" }, "Togo": { "en": "Togo", "fr": "Le Togo" }, "Uganda": { "en": "Uganda", "fr": "L’Ouganda" }, "Zambia": { "en": "Zambia", "fr": "La Zambie" }, "Zimbabwe": { "en": "Zimbabwe", "fr": "Le Zimbabwe" }, "SSA": { "en": "SSA", "fr": "L’Afrique Subsaharienne" }, "Sub-Saharan Africa": { "en": "Sub-Saharan Africa", "fr": "L’Afrique Subsaharienne" }, }, download: { en: "Download Data", fr: "Télécharger les Données" } } })3a:T80f,stylingGeneral = { const ns = Inputs.text().classList?.[0]; return htl.html`` }3b:T518,function tocOneliner({ selector = "h1", // selectors to include (can comma-separate, like "h1,h2,h3") heading = "In this notebook", // heading for ToC delim = " | ", // delimiter between links skip = [] // text content for headers to exclude } = {}) { // return a one-liner table of contents // allow for skips // modified from https://observablehq.com/@mbostock/toc return Generators.observe((notify) => { let headings = []; function observed() { let h = Array.from(document.querySelectorAll(selector)); h = h.filter((d) => !skip.includes(d.textContent)); if (h.length !== headings.length || h.some((h, i) => headings[i] !== h)) { notify( html`${heading}
${Array.from((headings = h), (h, i) => { const end = i === headings.length - 1; return Object.assign( html`${DOM.text(h.textContent)}${ end ? "" : delim }`, { onclick: (e) => (e.preventDefault(), h.scrollIntoView()) } ); })}` ); } } const observer = new MutationObserver(observed); observer.observe(document.body, { childList: true, subtree: true }); observed(); return () => observer.disconnect(); }); }3c:T271a,hs_icons = new Object({ human: html``, crop: html``, livestock: html`` })3d:T6626,country_flags = new Object({ Cameroon: html``, "Burkina Faso": html``, Tanzania: html``, Kenya: html``, Ethiopia: html``, Madagascar: html``, Mauritania: html``, Niger: html``, Ghana: html``, Zimbabwe: html``, "Sub-Saharan Africa": html``, Malawi: html``, Zambia: html``, Mali: html``, Benin: html``, Uganda: html``, Chad: html``, Senegal: html`` })3e:T7a12,climate_risk_icons = new Object({ "Extreme Weather Events": html``, "Climate Change": html``, Drought: html``, "Temperature increase": html``, "Heat Stress": html``, "Rainfall Variability": html``, Flood: html``, "Multiple Risks": html`` })3f:T4da,heatmap_legend = () => { const svg = d3.create("svg").attr("width", 200).attr("height", 60); const defs = svg.append("defs"); const linearGradient = defs .append("linearGradient") .attr("id", "linear-gradient"); linearGradient .attr("x1", "0%") .attr("y1", "0%") .attr("x2", "100%") .attr("y2", "0%"); linearGradient .append("stop") .attr("offset", "0%") .attr("stop-color", "#F7D732"); linearGradient .append("stop") .attr("offset", "100%") .attr("stop-color", "#216729"); svg .append("rect") .attr("width", 100) .attr("height", 15) .style("fill", "url(#linear-gradient)") .attr("transform", "translate(15,30)"); const legendScale = d3.scaleLinear().range([0, 100]).domain([0, 1]); const legendAxis = d3 .axisTop() .scale(legendScale) .tickValues([0, 1]) .tickFormat((d) => (d === 0 ? _lang(nbText.GenderedAfriAG.LegendScale.low) : _lang(nbText.GenderedAfriAG.LegendScale.high))) .tickSize(0); svg .append("g") .attr("class", "legend axis") .attr("transform", "translate(15,30)") .call(legendAxis) .style("font-size", "10px"); svg.selectAll(".axis path").style("stroke", "none"); return svg.node(); }40:T507,crop_gender_data = [ { country: "Tanzania", Cereals: 52, Legumes: 54, "Roots & Tubers": 52, "Fruits & Vegetables": 45, "Non-edible crops": 54 }, { country: "Malawi", Cereals: 54, Legumes: 53, "Roots & Tubers": 50, "Fruits & Vegetables": 49, "Non-edible crops": 47 }, { country: "Niger", Cereals: 21, Legumes: 28, "Roots & Tubers": null, "Fruits & Vegetables": 37, "Non-edible crops": null }, { country: "Uganda", Cereals: 55, Legumes: 59, "Roots & Tubers": 60, "Fruits & Vegetables": 53, "Non-edible crops": 50 }, { country: "Nigeria", Cereals: 36, Legumes: 40, "Roots & Tubers": 52, "Fruits & Vegetables": 38, "Non-edible crops": 46 }, // { // country: "N. Nigeria", // Cereals: 28, // Legumes: 28, // "Roots & Tubers": 50, // "Fruits & Vegetables": 36, // "Non-edible crops": 31 // }, // { // country: "S. Nigeria", // Cereals: 43, // Legumes: 51, // "Roots & Tubers": 54, // "Fruits & Vegetables": 39, // "Non-edible crops": 61 // }, { country: "Ethiopia", Cereals: 28, Legumes: 23, "Roots & Tubers": null, "Fruits & Vegetables": 38, "Non-edible crops": 32 } ]41:T6f7,// roi = (() => { // if (country !== "SSA") { // return non_topo.filter((d) => d.admin1_name === lollipop_admin); // } else if (country === "SSA" && lollipop_admin !== "SSA") { // const raw_data = non_topo.filter((d) => d.admin0_name === lollipop_admin); // const meanVariables = raw_data.reduce((acc, obj) => { // Object.keys(obj).forEach((key) => { // if (typeof obj[key] === "number" && !isNaN(obj[key])) { // acc[key] = (acc[key] || 0) + obj[key] / raw_data.length; // } // }); // return acc; // }, {}); // return [meanVariables]; // } else if (country === "SSA" && lollipop_admin === "SSA") { // const raw_data = non_topo; // const meanVariables = raw_data.reduce((acc, obj) => { // Object.keys(obj).forEach((key) => { // if (typeof obj[key] === "number" && !isNaN(obj[key])) { // acc[key] = (acc[key] || 0) + obj[key] / raw_data.length; // } // }); // return acc; // }, {}); // return [meanVariables]; // } // })() roi = { function calculateMeanVariables(raw_data) { return raw_data.reduce((acc, obj) => { Object.keys(obj).forEach((key) => { if (typeof obj[key] === "number" && !isNaN(obj[key])) { acc[key] = (acc[key] || 0) + obj[key] / raw_data.length; } }); return acc; }, {}); } let roiData; if (country !== "SSA") { if (admin1_sel === "Full Country" || admin1_sel === null) { const raw_data = non_topo.filter((d) => d.admin0_name === country); roiData = [calculateMeanVariables(raw_data)]; } else { roiData = non_topo.filter((d) => d.admin1_name === admin1_sel); } } else { roiData = [calculateMeanVariables(non_topo)]; } return roiData; }42:Tab6,bi_legend = () => { const k = 50; const arrow = "arrow-id"; d3.select(".biLegend").remove(); const tooltip = d3 .select("body") .append("div") .attr("class", `plotTooltip biLegend`) .style("position", "absolute") .style("opacity", 0); const svg = d3 .create("svg") .attr("width", 500) // increased size .attr("height", 500) // increased size .attr("viewBox", "0 0 525 525") // increased viewBox .attr("font-family", "sans-serif") .attr("font-size", 10); // const g = svg .append("g") .attr( "transform", `rotate(-45 ${(k * 3) / 2},${(k * 3) / 2}) translate(50, 100)` ); g.append("marker") .attr("id", arrow) .attr("markerHeight", 10) .attr("markerWidth", 10) .attr("refX", 6) .attr("refY", 3) .attr("orient", "auto") .append("path") .attr("d", "M0,0L9,3L0,6Z"); d3.cross(d3.range(3), d3.range(3)).forEach(([i, j]) => { const x = i * k; const y = (3 - 1 - j) * k; const fillColor = bi_color[j * 3 + i]; const titleText = `${_lang(nbText.GenderHotspots.selectors.GenderVar.domains[G_variable])}: ${_lang(nbText.General[labels[j]])}
${_lang(nbText.GenderHotspots.plot.heatLabels[heat_hazard])}: ${_lang(nbText.General[labels[i]])}`; g.append("rect") .attr("width", k) .attr("height", k) .attr("x", x) .attr("y", y) .attr("fill", fillColor) .on("mousemove", (event, d) => { tooltip .style("top", event.pageY - 10 + "px") .style("left", event.pageX + 1 + "px") .style("opacity", 1) .html(titleText); }) .on("mouseout", function (event, d) { mutable hover_data = null; tooltip.style("opacity", 0); }); }); g.append("line") .attr("marker-end", `url(#${arrow})`) .attr("x1", 0) .attr("x2", 3 * k) .attr("y1", 3 * k) .attr("y2", 3 * k) .attr("stroke", "black") .attr("stroke-width", 1.5); g.append("line") .attr("marker-end", `url(#${arrow})`) .attr("y2", 0) .attr("y1", 3 * k) .attr("stroke", "black") .attr("stroke-width", 1.5); g.append("text") // .attr("font-weight", "bold") .attr("font-size", "10px") .attr("dy", "0.71em") .attr("transform", `rotate(90) translate(${(3 / 2) * k},6)`) .attr("text-anchor", "middle") .text(_lang(nbText.GenderHotspots.selectors.GenderVar.domains[G_variable])); 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${notebookTitle}

`\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":4416,"value":"makeQuartoFlag(\"https://adaptationatlas.github.io/atlas_notebooksV1/notebooks/gender/notebook.html\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":4373,"value":"viewof prettyLanguageView = {\n return Inputs.bind(\n Inputs.radio(languages, {\n label: _lang({en: \"Language\", fr: \"Langue\"}),\n format: (d) => d.label\n }),\n viewof language\n );\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":939,"value":"tocOneliner({\n heading: `${_lang(nbText.toc.title)}`,\n skip: [notebookTitle, \"Appendix\"]\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":0,"value":"\n# ${_lang(nbText.overview.title)}\n\n${_lang(nbText.overview.text)}","pinned":false,"hidden":false,"mode":"md"},{"id":883,"value":"# ${_lang(nbText.GenderedAfriAG.title)}","pinned":false,"hidden":false,"mode":"md"},{"id":886,"value":"$${_lang(nbText.GenderedAfriAG.text)}","pinned":false,"hidden":false,"mode":"md"},{"id":2868,"value":"## ${_lang(nbText.GenderedAfriAG.FemaleParticipationLabor.title)}","pinned":false,"hidden":false,"mode":"md"},{"id":972,"value":"$31","pinned":false,"hidden":false,"mode":"ojs"},{"id":4366,"value":"buildInlineDownloadButton(task_gender_data, _lang(nbText.General.download), \"FemaleAgLaborParticipation\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":891,"value":"## ${_lang(nbText.GenderedAfriAG.WomenDominate.title)}","pinned":false,"hidden":false,"mode":"md"},{"id":894,"value":"$${_lang(nbText.GenderedAfriAG.WomenDominate.text)}","pinned":false,"hidden":false,"mode":"md"},{"id":4363,"value":"$32","pinned":false,"hidden":false,"mode":"ojs"},{"id":2858,"value":"## ${_lang(nbText.GenderedAfriAG.FemaleParticipationCrops.title)}","pinned":false,"hidden":false,"mode":"md"},{"id":896,"value":"$33","pinned":false,"hidden":false,"mode":"ojs"},{"id":4360,"value":"buildInlineDownloadButton(crop_gender_data, _lang(nbText.General.download), \"FemaleCropParticipation\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":898,"value":"# ${_lang(nbText.GenderInvestments.title)} ","pinned":false,"hidden":false,"mode":"md"},{"id":900,"value":"$${_lang(nbText.GenderInvestments.text)}","pinned":false,"hidden":false,"mode":"md"},{"id":1769,"value":"admin_form = {\n return Inputs.form(\n [\n Inputs.bind(Inputs.select(a0_list, { label: _lang(nbText.General.country), format: (v) => _lang(nbText.General.adminNames[v]) }), viewof country),\n Inputs.bind(\n Inputs.select(a1_list, { label: _lang(nbText.General.region), format: (v) => v === \"Full Country\" ? _lang(nbText.General.adminNames[v]): v }),\n viewof admin1_sel\n )\n ],\n {\n template: (inputs) =>\n htl.html`

${inputs}

`\n }\n );\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":2874,"value":"## ${_lang(nbText.GenderInvestments.title)}","pinned":false,"hidden":false,"mode":"md"},{"id":909,"value":"$34","pinned":false,"hidden":false,"mode":"ojs"},{"id":4354,"value":"buildInlineDownloadButton(non_topo, _lang(nbText.General.download), \"FemaleEmpowerment\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":918,"value":"# ${_lang(nbText.GenderHotspots.title)}\n\n${_lang(nbText.GenderHotspots.text)}","pinned":false,"hidden":false,"mode":"md"},{"id":3975,"value":"$35","pinned":false,"hidden":false,"mode":"ojs"},{"id":1842,"value":"viewof bivar_form = Inputs.form(\n {\n country: Inputs.bind(\n Inputs.select(a0_list, {\n label: _lang(nbText.General.country),\n value: clickedAdmin0,\n format: (v) => _lang(nbText.General.adminNames[v]) \n }),\n viewof country\n ),\n // Gender_var: Inputs.bind(\n // Inputs.select(\n // new Map(Object.entries(F_dict).map(([key, value]) => [value, key])),\n // { label: \"Gender Variable\", value: \"women_in_agric\" }\n // ),\n // viewof sel_gender_var\n // ),\n scenario: Inputs.select(\n // new Map(\n // Object.entries(scenario_dict).map(([key, value]) => [value, key])\n // ),\n Object.keys(scenario_dict),\n {\n label: _lang(nbText.General.scenario),\n format: (x) => x === \"historic\" ? _lang(nbText.General.historic) : scenario_dict[x]\n }\n )\n },\n {\n template: (inputs) => htl.html`

\n ${Object.values(inputs)}\n

`\n }\n)","pinned":false,"hidden":false,"mode":"ojs"},{"id":168,"value":"$36","pinned":false,"hidden":false,"mode":"ojs"},{"id":4349,"value":"buildInlineDownloadButton(BiVar_merge, _lang(nbText.General.download), \"HazardGenderIntersection\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":4348,"value":"$37","pinned":false,"hidden":false,"mode":"ojs"},{"id":1401,"value":"# ${_lang(nbText.BridgeGap.title)}\n\n${_lang(nbText.BridgeGap.text)}","pinned":false,"hidden":false,"mode":"md"},{"id":2695,"value":"viewof adaptation = Inputs.select(adaptationOptions, {\n label: _lang(nbText.BridgeGap.selector.title),\n format: (l) => _lang(nbText.BridgeGap.selector.options[l])\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":2593,"value":"$38","pinned":false,"hidden":false,"mode":"ojs"},{"id":2463,"value":"# ${_lang(nbText.Summary.title)}\n\n${_lang(nbText.Summary.text)}","pinned":false,"hidden":false,"mode":"md"},{"id":2043,"value":"# ${_lang(nbText.MethodsSources.title)}","pinned":false,"hidden":false,"mode":"md"},{"id":3571,"value":"--- \n## ${_lang(nbText.MethodsSources.datasets.title)}","pinned":false,"hidden":false,"mode":"md"},{"id":3576,"value":"### ${_lang(nbText.MethodsSources.datasets.femaleParticipation.title)}\n\n${_lang(nbText.MethodsSources.datasets.femaleParticipation.text)}","pinned":false,"hidden":false,"mode":"md"},{"id":3587,"value":"### ${_lang(nbText.MethodsSources.datasets.heatStress.title)}\n${_lang(nbText.MethodsSources.datasets.heatStress.text)}","pinned":false,"hidden":false,"mode":"md"},{"id":3596,"value":"### ${_lang(nbText.MethodsSources.datasets.FemaleWellbeing.title)}\n\n${_lang(nbText.MethodsSources.datasets.FemaleWellbeing.text)}\n","pinned":false,"hidden":false,"mode":"md"},{"id":3601,"value":"### ${_lang(nbText.MethodsSources.datasets.WomenInAg.title)}\n\n${_lang(nbText.MethodsSources.datasets.WomenInAg.text)}","pinned":false,"hidden":false,"mode":"md"},{"id":3603,"value":"### ${_lang(nbText.MethodsSources.datasets.adaptationSolutions.title)}\n\n${_lang(nbText.MethodsSources.datasets.adaptationSolutions.text)}","pinned":false,"hidden":false,"mode":"md"},{"id":3607,"value":"### ${_lang(nbText.MethodsSources.datasets.boundaries.title)}\n${_lang(nbText.MethodsSources.datasets.boundaries.text)}","pinned":false,"hidden":false,"mode":"md"},{"id":3610,"value":"## ${_lang(nbText.MethodsSources.methods.title)}","pinned":false,"hidden":false,"mode":"md"},{"id":3642,"value":"### ${_lang(nbText.MethodsSources.methods.WomenInAg.title)}\n${_lang(nbText.MethodsSources.methods.WomenInAg.text)}","pinned":false,"hidden":false,"mode":"md"},{"id":3633,"value":"### ${_lang(nbText.MethodsSources.methods.hotspots.title)}\n\n${_lang(nbText.MethodsSources.methods.hotspots.text)}","pinned":false,"hidden":false,"mode":"md"},{"id":3612,"value":"## ${_lang(nbText.MethodsSources.methods.solutions.title)}\n\n${_lang(nbText.MethodsSources.methods.solutions.text)}","pinned":false,"hidden":false,"mode":"md"},{"id":2092,"value":"# ${_lang(nbText.Appendix.title)}","pinned":false,"hidden":false,"mode":"md"},{"id":3302,"value":"---\n## Work by Topic\n*Cells that power different sections*","pinned":false,"hidden":false,"mode":"md"},{"id":2096,"value":"---\n### Support notebook\n[Support Notebook](https://observablehq.com/@periscopic/ab-atlas-spotlight-support): used for imports across spotlights.","pinned":false,"hidden":false,"mode":"md"},{"id":2101,"value":"---\n### Notebook Variables\n_Main Variables across the notebook_","pinned":false,"hidden":false,"mode":"md"},{"id":932,"value":"notebookTitle = \"Analyze Women's Exposure To Climate Hazards\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":4329,"value":"import {buildInlineDownloadButton} from \"1bda5f9b60c64223\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":4328,"value":"## Language and Translation tools","pinned":false,"hidden":false,"mode":"md"},{"id":4327,"value":"import {Lang} from \"7d46679f7d0d917e\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":4326,"value":"_lang = Lang.lg(language.key)","pinned":false,"hidden":false,"mode":"ojs"},{"id":4325,"value":"languages = [\n { key: \"en\", label: \"English\", locale: 'en-US' },\n { key: \"fr\", label: \"Français\", locale: 'fr-FR' }\n]","pinned":false,"hidden":false,"mode":"ojs"},{"id":4324,"value":"defaultLangKey = {\n const name = \"lang\";\n const list = languages.map((d) => d.key);\n const defaultKey = \"en\";\n\n const queryParam = await Lang.getParamFromList({ name, list });\n\n return queryParam ?? defaultKey;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":4323,"value":"viewof language = Inputs.radio(languages, {\n label: \"Main language toggle\",\n format: (d) => d.key,\n value: languages.find((x) => x.key === defaultLangKey),\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":4322,"value":"$39","pinned":false,"hidden":false,"mode":"ojs"},{"id":2106,"value":"---\n### Notebook Styling\n*styling helpers*\n","pinned":false,"hidden":false,"mode":"md"},{"id":2110,"value":"**CSS styling**","pinned":false,"hidden":false,"mode":"md"},{"id":4260,"value":"// full styling\nstyling = md`${stylingGeneral}${stylingTooltip}`","pinned":false,"hidden":false,"mode":"ojs"},{"id":3035,"value":"stylingTooltip = htl.html``","pinned":false,"hidden":false,"mode":"ojs"},{"id":2365,"value":"$3a","pinned":false,"hidden":false,"mode":"ojs"},{"id":2112,"value":"**Table Of Contents**","pinned":false,"hidden":false,"mode":"md"},{"id":2289,"value":"$3b","pinned":false,"hidden":false,"mode":"ojs"},{"id":5,"value":"---\n### Notebook data\n_Datasets for use across the notebook_","pinned":false,"hidden":false,"mode":"md"},{"id":64,"value":"db = {\n const db = await DuckDBClient.of({\n gender: FileAttachment(\"a0a1_gender_variables.parquet\"),\n hazards: FileAttachment(\"HS_hazards.parquet\"),\n mapspam_vop: FileAttachment(\"mapspam_vop.parquet\")\n });\n return db;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":69,"value":"hazard_data = db.query(`Select admin0_name, admin1_name, scenario, timeframe as time, value as hs_value, hazard\n FROM hazards\n WHERE hazard LIKE '%${hazard_dict[heat_hazard]}%'\n AND admin2_name IS NULL`)","pinned":false,"hidden":false,"mode":"ojs"},{"id":98,"value":"gender_data = db.query(`Select *\n FROM gender`)\n// WHERE admin2_name IS NULL`)","pinned":false,"hidden":false,"mode":"ojs"},{"id":85,"value":"#### Dictionaries\n_Used to provide consistent variable names and thresholds across the notebook_","pinned":false,"hidden":false,"mode":"md"},{"id":91,"value":"F_dict = new Object({\n women_in_agric: \"Women in Agriculture\",\n empowerment_index: \"Empowerment Index\",\n education: \"Education\",\n // domestic_violence: \"Domestic Violence\",\n domestic_violence: \"Domestic Safety\",\n reproductive_health: \"Reproductive Health\",\n family_planning: \"Family Planning\",\n employment: \"Employment\",\n decisions: \"Decision Making\"\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":88,"value":"hazard_dict = new Object({\n human: \"HSH\",\n crop: \"NTx35\",\n livestock: \"THI\"\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":342,"value":"hazard_bivariate_thresholds = new Object({\n human: { low: 27, high: 42 },\n livestock: { low: 72, high: 90 },\n crop: { low: 9, high: 25 }\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":1851,"value":"scenario_dict = new Object({\n historic: \"Historic\",\n ssp245: \"SSP245\",\n ssp585: \"SSP585\"\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":2503,"value":"crop_groups = new Object({\n \"Roots & Tubers\": [\"'potato'\", \"'sweet potato'\", \"'yams'\", \"'cassava'\"],\n Cereals: [\n \"'wheat'\",\n \"'rice'\",\n \"'maize'\",\n \"'barley'\",\n \"'pearl millet'\",\n \"'small millet'\",\n \"'sorghum'\"\n ],\n Legumes: [\n \"'bean'\",\n \"'chickpea'\",\n \"'cowpea'\",\n \"'pigeonpea'\",\n \"'lentil'\",\n \"'groundnut'\",\n \"'soybean'\"\n ],\n \"Fruits & Vegetables\": [\"'plantain'\", \"'coconut'\", \"'banana'\"],\n \"Non-edible crops\": [\n \"'cocoa'\",\n \"'rapeseed'\",\n \"'sesameseed'\",\n \"'oilpalm'\",\n \"'cotton'\",\n \"'tea'\",\n \"'tobacco'\",\n \"'arabica coffee'\",\n \"'sunflower'\",\n \"'sugarcane'\",\n \"'sugarbeet'\",\n \"'robusta coffee'\"\n ]\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":9,"value":"#### Icons\n*Icons used across the notebook for the heat stress radio buttons and in the solution table*","pinned":false,"hidden":false,"mode":"md"},{"id":11,"value":"$3c","pinned":false,"hidden":false,"mode":"ojs"},{"id":2571,"value":"$3d","pinned":false,"hidden":false,"mode":"ojs"},{"id":2576,"value":"$3e","pinned":false,"hidden":false,"mode":"ojs"},{"id":2048,"value":"---\n### Admin Data and Selection\n*Data imports for the Administrative boundaries at Country (admin 0) and admin 1 level.*","pinned":false,"hidden":false,"mode":"md"},{"id":50,"value":"admin1_boundaries = FileAttachment(\n \"atlas-region_admin1_harmonizedV2.json\"\n).json()","pinned":false,"hidden":false,"mode":"ojs"},{"id":355,"value":"admin0_boundaries = FileAttachment(\"atlas-region_admin0_harmonized.json\").json()","pinned":false,"hidden":false,"mode":"ojs"},{"id":2124,"value":"a0_list = [\"SSA\"].concat(\n Array.from(\n new Set(\n Object.values(\n admin0_boundaries.objects[\"atlas-region_admin0_harmonized\"].geometries\n ).map((d) => d.properties.admin0_name)\n )\n )\n)","pinned":false,"hidden":false,"mode":"ojs"},{"id":2128,"value":"a1_list = {\n const filteredData = admin1_boundaries.objects[\n \"atlas-region_admin1_harmonizedV2\"\n ].geometries.filter((d) => d.properties.admin0_name === country);\n\n const a1Names = Array.from(\n new Set(filteredData.map((d) => d.properties.admin1_name))\n );\n return [\"Full Country\"].concat(a1Names);\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":2134,"value":"mutable clickedAdmin0 = null","pinned":false,"hidden":false,"mode":"ojs"},{"id":1755,"value":"viewof country = {\n // Create the selection input.\n const country = Inputs.select(a0_list, {\n // label: html`Select Country:`,\n label: \"Select Country\",\n value: clickedAdmin0\n });\n\n // Update clickedAdmin0 when the selection changes.\n country.addEventListener(\"input\", () => {\n mutable clickedAdmin0 = country.value === \"SSA\" ? null : country.value;\n });\n return country;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1762,"value":"viewof admin1_sel = Inputs.select(a1_list, { label: \"admin1\" })","pinned":false,"hidden":false,"mode":"ojs"},{"id":1005,"value":"---\n### Heat maps","pinned":false,"hidden":false,"mode":"md"},{"id":1216,"value":"$3f","pinned":false,"hidden":false,"mode":"ojs"},{"id":1156,"value":"task_gender_data = [\n {\n country: \"Tanzania\",\n \"Land Prep.\": 52,\n \"Planting/Weeding\": 53,\n Harvesting: 54\n },\n {\n country: \"Malawi\",\n \"Land Prep.\": 53,\n \"Planting/Weeding\": 53,\n Harvesting: 51\n },\n {\n country: \"Niger\",\n \"Land Prep.\": 18,\n \"Planting/Weeding\": 25,\n Harvesting: 28\n },\n {\n country: \"Uganda\",\n \"Land Prep.\": null,\n \"Planting/Weeding\": null,\n Harvesting: null\n },\n {\n country: \"Nigeria\",\n \"Land Prep.\": 41,\n \"Planting/Weeding\": 41,\n Harvesting: 42\n },\n {\n country: \"Ethiopia\",\n \"Land Prep.\": 26,\n \"Planting/Weeding\": 26,\n Harvesting: 37\n }\n]","pinned":false,"hidden":false,"mode":"ojs"},{"id":1007,"value":"$40","pinned":false,"hidden":false,"mode":"ojs"},{"id":1184,"value":"crop_gender_means = new Object({\n crop_mean: {\n Cereals: 31,\n Legumes: 31,\n \"Roots & Tubers\": 42,\n \"Fruits & Vegetables\": 34,\n \"Non-edible Crops\": 33\n },\n country_mean: {\n Tanzania: 52,\n Malawi: 52,\n Niger: 24,\n Uganda: 56,\n \"N. Nigeria\": 32,\n \"S. Nigeria\": 52,\n Ethiopia: 29\n }\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":1175,"value":"mutable hover_data = null","pinned":false,"hidden":false,"mode":"ojs"},{"id":2424,"value":"#### Dynamic Insights for the heat maps","pinned":false,"hidden":false,"mode":"md"},{"id":2430,"value":"root_tube_total = {\n const total = await db.query(\n `SELECT SUM(\"value\") as rt_sum, admin0_name\n FROM \"mapspam_vop\"\n WHERE crop IN (${crop_groups[\"Roots & Tubers\"]})\n GROUP BY admin0_name`\n );\n\n const women_total = total.map((d) => {\n const match = crop_gender_data.find((item) => item.country === d.admin0_name);\n return match && match['Roots & Tubers'] ? d.rt_sum * (match['Roots & Tubers']/100) : null;\n });\n\n const sum_W_total = women_total.reduce((acc, val) => acc + (val || 0), 0)\n \n\n return sum_W_total;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":2499,"value":"selection_total = {\n if (crop_cat === null) {\n return null;\n } else {\n const total = await db.query(\n `Select SUM(\"value\") as crop_sum\n FROM mapspam_vop\n WHERE admin0_name LIKE '%${hover_data.country}'\n AND crop in (${crop_groups[crop_cat]})`\n );\n return Math.round(total[0][\"crop_sum\"]);\n }\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":2526,"value":"crop_cat = {\n if (hover_data === null) {\n return null;\n } else {\n return hover_data.type;\n }\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":847,"value":"---\n### Lollipop Plot","pinned":false,"hidden":false,"mode":"md"},{"id":849,"value":"$41","pinned":false,"hidden":false,"mode":"ojs"},{"id":862,"value":"regional_avg = {\n const surrounding = non_topo.filter((d) => d.admin1_name !== \"na\");\n\n const variablesToKeep = [\n // \"women_in_agric\",\n // \"female_empowerment\",\n \"decisions\",\n \"domestic_violence\",\n \"education\",\n \"employment\",\n \"family_planning\",\n \"empowerment_index\",\n \"reproductive_health\"\n ];\n\n // Calculate the mean value for each variable\n const meanValues = variablesToKeep.reduce((acc, variable) => {\n const sum = surrounding.reduce((sum, area) => sum + area[variable], 0);\n acc[variable] = sum / surrounding.length;\n return acc;\n }, {});\n\n return meanValues;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":853,"value":"// non_topo = {\n// if (country === \"SSA\" || admin1_sel === \"Full Country\") {\n// return BiVar_merge;\n// } else {\n// return BiVar_merge.filter((d) => d.admin0_name === country);\n// }\n// }\nnon_topo = {\n if (country === \"SSA\" || admin1_sel === \"Full Country\") {\n return db.query(`SELECT * FROM gender`);\n } else {\n return db.query(`SELECT * FROM gender WHERE admin0_name LIKE '${country}'`);\n }\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1963,"value":"loli_legend = () => {\n const svg = d3.create(\"svg\").attr(\"width\", 200).attr(\"height\", 100);\n\n const box = svg\n .append(\"rect\")\n .attr(\"width\", 200)\n .attr(\"height\", 100)\n .attr(\"rx\", 10)\n .attr(\"ry\", 10)\n .attr(\"fill\", \"#f0f0f0\");\n\n const color = d3\n .scaleOrdinal()\n .domain([\"Regional average\", \"Admin average\"])\n .range([\"#216729\", \"grey\"]);\n\n const circles = svg\n .selectAll(\"circle\")\n .data([\"Regional average\", \"Admin average\"])\n .join(\"circle\")\n .attr(\"cx\", 30)\n .attr(\"cy\", (d, i) => 35 + i * 30)\n .attr(\"r\", 6)\n .attr(\"fill\", (d) => color(d));\n\n let region_name;\n if (admin1_sel !== \"Full Country\") {\n region_name = admin1_sel;\n }\n\n const labels = svg\n .selectAll(\"text\")\n // .data([region_name + \" average\", country + \" average\"])\n .data([\"regional_avg\", \"national_avg\"])\n .join(\"text\")\n .attr(\"x\", 45)\n .attr(\"y\", (d, i) => 40 + i * 30)\n .text((d) => _lang(nbText.General[d]));\n\n return svg.node();\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":109,"value":"---\n### Bivariate Choropleth map\n","pinned":false,"hidden":false,"mode":"md"},{"id":3790,"value":"viewof sel_gender_var = Inputs.select(\n new Map(Object.entries(F_dict).map(([key, value]) => [value, key])),\n { label: \"Variable\", value: \"women_in_agric\" }\n)","pinned":false,"hidden":false,"mode":"ojs"},{"id":1859,"value":"ssp = bivar_form[\"scenario\"]","pinned":false,"hidden":false,"mode":"ojs"},{"id":4022,"value":"heat_hazard = BiVariable_form[\"hazard\"]","pinned":false,"hidden":false,"mode":"ojs"},{"id":1861,"value":"G_variable = BiVariable_form[\"Gender_var\"]","pinned":false,"hidden":false,"mode":"ojs"},{"id":156,"value":"index = d3.index(\n BiVar_merge,\n (d) => d.admin0_name,\n (d) => d.admin1_name\n)","pinned":false,"hidden":false,"mode":"ojs"},{"id":133,"value":"BiVar_merge = {\n // const hazard_regex = /(_mean$ |/i;\n const filtered_haz = hazard_data.filter(\n (d) =>\n d.scenario === ssp && //Note the ! operator i nnext line\n !d.hazard.includes(\"mean\") && // could be mean or max\n (d.time === \"2041_2060\" || d.time === \"historic\")\n // d.admin1_name !== null //hazard_regex.test(d.hazard)\n );\n const mergedData = filtered_haz.map((item2) => {\n const matchingItem = gender_data.find(\n (item1) =>\n item1[\"admin0_name\"] === item2[\"admin0_name\"] &&\n item1[\"admin1_name\"] === item2[\"admin1_name\"]\n );\n\n return matchingItem ? { ...matchingItem, ...item2 } : item2;\n });\n\n return mergedData;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1814,"value":"filtered_haz = hazard_data.filter(\n (d) => d.scenario === 'historic' && !d.hazard.includes(\"max\") //&&\n // d.admin1_name !== null //hazard_regex.test(d.hazard)\n)","pinned":false,"hidden":false,"mode":"ojs"},{"id":111,"value":"bi_color = {\n if (G_variable === \"women_in_agric\") {\n return [\n \"#e8e8e8\",\n \"#e4acac\",\n \"#c85a5a\",\n \"#b0d5df\",\n \"#ad9ea5\",\n \"#985356\",\n \"#64acbe\",\n \"#627f8c\",\n \"#574249\"\n ];\n } else {\n return [\n \"#e8e8e8\",\n \"#ddb395\",\n \"#d17a3d\",\n \"#a7c4d4\",\n \"#a7b395\",\n \"#a77a3d\",\n \"#65a0bf\",\n \"#65a095\",\n \"#657a3d\"\n ];\n }\n}\n\n// bi_color = [\n// \"#e8e8e8\",\n// \"#e4acac\",\n// \"#c85a5a\",\n// \"#b0d5df\",\n// \"#ad9ea5\",\n// \"#985356\",\n// \"#64acbe\",\n// \"#627f8c\",\n// \"#574249\"\n// ]\n\n// bi_color = [\n// \"#574249\",\n// \"#985356\",\n// \"#c85a5a\",\n// \"#627f8c\",\n// \"#ad9ea5\",\n// \"#e4acac\",\n// \"#64acbe\",\n// \"#b0d5df\",\n// \"#e8e8e8\"\n//\n\n// bi_color = [\n// \"#e8e8e8\",\n// \"#ddb395\",\n// \"#d17a3d\",\n// \"#a7c4d4\",\n// \"#a7b395\",\n// \"#a77a3d\",\n// \"#65a0bf\",\n// \"#65a095\",\n// \"#657a3d\"\n// ]","pinned":false,"hidden":false,"mode":"ojs"},{"id":114,"value":"labels = [\"low\", \"moderate\", \"high\"]","pinned":false,"hidden":false,"mode":"ojs"},{"id":116,"value":"$42","pinned":false,"hidden":false,"mode":"ojs"},{"id":2583,"value":"---\n### Solutions Table","pinned":false,"hidden":false,"mode":"md"},{"id":2585,"value":"gender_solutions = {\n const raw_csv = await FileAttachment(\"sectorSolutions_gender.csv\").csv();\n return raw_csv.map((d) => {\n d[\"Confidence in Result\"] = d[\"Agreement\"];\n d[\"Gender Outcome Score\"] = d[\"Gender outcome score\"];\n delete d[\"Agreement\"];\n delete d['Gender outcome score']\n return d;\n });\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":2691,"value":"adaptationOptions = Array.from(\n new Set(gender_solutions.map((d) => d[\"Adaptation Options\"]))\n)","pinned":false,"hidden":false,"mode":"ojs"},{"id":3111,"value":"formatDollar = new Intl.NumberFormat(\"en-US\", {\n notation: \"compact\",\n compactDisplay: \"short\",\n style: \"currency\",\n currency: \"usd\"\n}).format","pinned":false,"hidden":false,"mode":"ojs"},{"id":4413,"value":"makeQuartoFlag = (url) => {\n const baseText = _lang(\n {\n en: \"New version available! This notebook has been rebuilt in Quarto for a better experience:\",\n fr: \"Nouvelle version disponible ! Ce carnet a été reconstruit avec Quarto pour une meilleure expérience:\"\n }\n )\n const openText = _lang(\n {\n en: \"Open Updated Notebook\",\n fr: \"Ouvrir le carnet mis à jour\"\n }\n )\n \n return htl.html`\n

\n ${baseText} ${openText} →\n

\n `\n}","pinned":false,"hidden":false,"mode":"ojs"}],"files":"$W43","stdlib":"1","titling":"auto"}